BRNSystems/Info_edupage
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Revert "Delete .venv directory"

Browse Source 
This reverts commit 5a2693bd9f.
This commit is contained in:
Untriex Programming
2021-08-31 22:26:50 +02:00
parent fd493a708d
commit 6544d16770
5105 changed files with 1440072 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

910
.venv/lib/python3.9/site-packages/numpy/LICENSE.txt Normal file
View File

@@ -0,0 +1,910 @@
Copyright (c) 2005-2021, NumPy Developers.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following
disclaimer in the documentation and/or other materials provided
with the distribution.
* Neither the name of the NumPy Developers nor the names of any
contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
----
This binary distribution of NumPy also bundles the following software:
Name: OpenBLAS
Files: .libs/libopenb*.so
Description: bundled as a dynamically linked library
Availability: https://github.com/xianyi/OpenBLAS/
License: 3-clause BSD
Copyright (c) 2011-2014, The OpenBLAS Project
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in
the documentation and/or other materials provided with the
distribution.
3. Neither the name of the OpenBLAS project nor the names of
its contributors may be used to endorse or promote products
derived from this software without specific prior written
permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Name: LAPACK
Files: .libs/libopenb*.so
Description: bundled in OpenBLAS
Availability: https://github.com/xianyi/OpenBLAS/
License 3-clause BSD
Copyright (c) 1992-2013 The University of Tennessee and The University
of Tennessee Research Foundation. All rights
reserved.
Copyright (c) 2000-2013 The University of California Berkeley. All
rights reserved.
Copyright (c) 2006-2013 The University of Colorado Denver. All rights
reserved.
$COPYRIGHT$
Additional copyrights may follow
$HEADER$
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer listed
in this license in the documentation and/or other materials
provided with the distribution.
- Neither the name of the copyright holders nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
The copyright holders provide no reassurances that the source code
provided does not infringe any patent, copyright, or any other
intellectual property rights of third parties. The copyright holders
disclaim any liability to any recipient for claims brought against
recipient by any third party for infringement of that parties
intellectual property rights.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Name: GCC runtime library
Files: .libs/libgfortran*.so
Description: dynamically linked to files compiled with gcc
Availability: https://gcc.gnu.org/viewcvs/gcc/
License: GPLv3 + runtime exception
Copyright (C) 2002-2017 Free Software Foundation, Inc.
Libgfortran is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 3, or (at your option)
any later version.
Libgfortran is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
Under Section 7 of GPL version 3, you are granted additional
permissions described in the GCC Runtime Library Exception, version
3.1, as published by the Free Software Foundation.
You should have received a copy of the GNU General Public License and
a copy of the GCC Runtime Library Exception along with this program;
see the files COPYING3 and COPYING.RUNTIME respectively. If not, see
<http://www.gnu.org/licenses/>.
----
Full text of license texts referred to above follows (that they are
listed below does not necessarily imply the conditions apply to the
present binary release):
----
GCC RUNTIME LIBRARY EXCEPTION
Version 3.1, 31 March 2009
Copyright (C) 2009 Free Software Foundation, Inc. <http://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies of this
license document, but changing it is not allowed.
This GCC Runtime Library Exception ("Exception") is an additional
permission under section 7 of the GNU General Public License, version
3 ("GPLv3"). It applies to a given file (the "Runtime Library") that
bears a notice placed by the copyright holder of the file stating that
the file is governed by GPLv3 along with this Exception.
When you use GCC to compile a program, GCC may combine portions of
certain GCC header files and runtime libraries with the compiled
program. The purpose of this Exception is to allow compilation of
non-GPL (including proprietary) programs to use, in this way, the
header files and runtime libraries covered by this Exception.
0. Definitions.
A file is an "Independent Module" if it either requires the Runtime
Library for execution after a Compilation Process, or makes use of an
interface provided by the Runtime Library, but is not otherwise based
on the Runtime Library.
"GCC" means a version of the GNU Compiler Collection, with or without
modifications, governed by version 3 (or a specified later version) of
the GNU General Public License (GPL) with the option of using any
subsequent versions published by the FSF.
"GPL-compatible Software" is software whose conditions of propagation,
modification and use would permit combination with GCC in accord with
the license of GCC.
"Target Code" refers to output from any compiler for a real or virtual
target processor architecture, in executable form or suitable for
input to an assembler, loader, linker and/or execution
phase. Notwithstanding that, Target Code does not include data in any
format that is used as a compiler intermediate representation, or used
for producing a compiler intermediate representation.
The "Compilation Process" transforms code entirely represented in
non-intermediate languages designed for human-written code, and/or in
Java Virtual Machine byte code, into Target Code. Thus, for example,
use of source code generators and preprocessors need not be considered
part of the Compilation Process, since the Compilation Process can be
understood as starting with the output of the generators or
preprocessors.
A Compilation Process is "Eligible" if it is done using GCC, alone or
with other GPL-compatible software, or if it is done without using any
work based on GCC. For example, using non-GPL-compatible Software to
optimize any GCC intermediate representations would not qualify as an
Eligible Compilation Process.
1. Grant of Additional Permission.
You have permission to propagate a work of Target Code formed by
combining the Runtime Library with Independent Modules, even if such
propagation would otherwise violate the terms of GPLv3, provided that
all Target Code was generated by Eligible Compilation Processes. You
may then convey such a combination under terms of your choice,
consistent with the licensing of the Independent Modules.
2. No Weakening of GCC Copyleft.
The availability of this Exception does not imply any general
presumption that third-party software is unaffected by the copyleft
requirements of the license of GCC.
----
GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU General Public License is a free, copyleft license for
software and other kinds of works.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
the GNU General Public License is intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users. We, the Free Software Foundation, use the
GNU General Public License for most of our software; it applies also to
any other work released this way by its authors. You can apply it to
your programs, too.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
free programs, and that you know you can do these things.
To protect your rights, we need to prevent others from denying you
these rights or asking you to surrender the rights. Therefore, you have
certain responsibilities if you distribute copies of the software, or if
you modify it: responsibilities to respect the freedom of others.
For example, if you distribute copies of such a program, whether
gratis or for a fee, you must pass on to the recipients the same
freedoms that you received. You must make sure that they, too, receive
or can get the source code. And you must show them these terms so they
know their rights.
Developers that use the GNU GPL protect your rights with two steps:
(1) assert copyright on the software, and (2) offer you this License
giving you legal permission to copy, distribute and/or modify it.
For the developers' and authors' protection, the GPL clearly explains
that there is no warranty for this free software. For both users' and
authors' sake, the GPL requires that modified versions be marked as
changed, so that their problems will not be attributed erroneously to
authors of previous versions.
Some devices are designed to deny users access to install or run
modified versions of the software inside them, although the manufacturer
can do so. This is fundamentally incompatible with the aim of
protecting users' freedom to change the software. The systematic
pattern of such abuse occurs in the area of products for individuals to
use, which is precisely where it is most unacceptable. Therefore, we
have designed this version of the GPL to prohibit the practice for those
products. If such problems arise substantially in other domains, we
stand ready to extend this provision to those domains in future versions
of the GPL, as needed to protect the freedom of users.
Finally, every program is threatened constantly by software patents.
States should not allow patents to restrict development and use of
software on general-purpose computers, but in those that do, we wish to
avoid the special danger that patents applied to a free program could
make it effectively proprietary. To prevent this, the GPL assures that
patents cannot be used to render the program non-free.
The precise terms and conditions for copying, distribution and
modification follow.
TERMS AND CONDITIONS
0. Definitions.
"This License" refers to version 3 of the GNU General Public License.
"Copyright" also means copyright-like laws that apply to other kinds of
works, such as semiconductor masks.
"The Program" refers to any copyrightable work licensed under this
License. Each licensee is addressed as "you". "Licensees" and
"recipients" may be individuals or organizations.
To "modify" a work means to copy from or adapt all or part of the work
in a fashion requiring copyright permission, other than the making of an
exact copy. The resulting work is called a "modified version" of the
earlier work or a work "based on" the earlier work.
A "covered work" means either the unmodified Program or a work based
on the Program.
To "propagate" a work means to do anything with it that, without
permission, would make you directly or secondarily liable for
infringement under applicable copyright law, except executing it on a
computer or modifying a private copy. Propagation includes copying,
distribution (with or without modification), making available to the
public, and in some countries other activities as well.
To "convey" a work means any kind of propagation that enables other
parties to make or receive copies. Mere interaction with a user through
a computer network, with no transfer of a copy, is not conveying.
An interactive user interface displays "Appropriate Legal Notices"
to the extent that it includes a convenient and prominently visible
feature that (1) displays an appropriate copyright notice, and (2)
tells the user that there is no warranty for the work (except to the
extent that warranties are provided), that licensees may convey the
work under this License, and how to view a copy of this License. If
the interface presents a list of user commands or options, such as a
menu, a prominent item in the list meets this criterion.
1. Source Code.
The "source code" for a work means the preferred form of the work
for making modifications to it. "Object code" means any non-source
form of a work.
A "Standard Interface" means an interface that either is an official
standard defined by a recognized standards body, or, in the case of
interfaces specified for a particular programming language, one that
is widely used among developers working in that language.
The "System Libraries" of an executable work include anything, other
than the work as a whole, that (a) is included in the normal form of
packaging a Major Component, but which is not part of that Major
Component, and (b) serves only to enable use of the work with that
Major Component, or to implement a Standard Interface for which an
implementation is available to the public in source code form. A
"Major Component", in this context, means a major essential component
(kernel, window system, and so on) of the specific operating system
(if any) on which the executable work runs, or a compiler used to
produce the work, or an object code interpreter used to run it.
The "Corresponding Source" for a work in object code form means all
the source code needed to generate, install, and (for an executable
work) run the object code and to modify the work, including scripts to
control those activities. However, it does not include the work's
System Libraries, or general-purpose tools or generally available free
programs which are used unmodified in performing those activities but
which are not part of the work. For example, Corresponding Source
includes interface definition files associated with source files for
the work, and the source code for shared libraries and dynamically
linked subprograms that the work is specifically designed to require,
such as by intimate data communication or control flow between those
subprograms and other parts of the work.
The Corresponding Source need not include anything that users
can regenerate automatically from other parts of the Corresponding
Source.
The Corresponding Source for a work in source code form is that
same work.
2. Basic Permissions.
All rights granted under this License are granted for the term of
copyright on the Program, and are irrevocable provided the stated
conditions are met. This License explicitly affirms your unlimited
permission to run the unmodified Program. The output from running a
covered work is covered by this License only if the output, given its
content, constitutes a covered work. This License acknowledges your
rights of fair use or other equivalent, as provided by copyright law.
You may make, run and propagate covered works that you do not
convey, without conditions so long as your license otherwise remains
in force. You may convey covered works to others for the sole purpose
of having them make modifications exclusively for you, or provide you
with facilities for running those works, provided that you comply with
the terms of this License in conveying all material for which you do
not control copyright. Those thus making or running the covered works
for you must do so exclusively on your behalf, under your direction
and control, on terms that prohibit them from making any copies of
your copyrighted material outside their relationship with you.
Conveying under any other circumstances is permitted solely under
the conditions stated below. Sublicensing is not allowed; section 10
makes it unnecessary.
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
No covered work shall be deemed part of an effective technological
measure under any applicable law fulfilling obligations under article
11 of the WIPO copyright treaty adopted on 20 December 1996, or
similar laws prohibiting or restricting circumvention of such
measures.
When you convey a covered work, you waive any legal power to forbid
circumvention of technological measures to the extent such circumvention
is effected by exercising rights under this License with respect to
the covered work, and you disclaim any intention to limit operation or
modification of the work as a means of enforcing, against the work's
users, your or third parties' legal rights to forbid circumvention of
technological measures.
4. Conveying Verbatim Copies.
You may convey verbatim copies of the Program's source code as you
receive it, in any medium, provided that you conspicuously and
appropriately publish on each copy an appropriate copyright notice;
keep intact all notices stating that this License and any
non-permissive terms added in accord with section 7 apply to the code;
keep intact all notices of the absence of any warranty; and give all
recipients a copy of this License along with the Program.
You may charge any price or no price for each copy that you convey,
and you may offer support or warranty protection for a fee.
5. Conveying Modified Source Versions.
You may convey a work based on the Program, or the modifications to
produce it from the Program, in the form of source code under the
terms of section 4, provided that you also meet all of these conditions:
a) The work must carry prominent notices stating that you modified
it, and giving a relevant date.
b) The work must carry prominent notices stating that it is
released under this License and any conditions added under section
7. This requirement modifies the requirement in section 4 to
"keep intact all notices".
c) You must license the entire work, as a whole, under this
License to anyone who comes into possession of a copy. This
License will therefore apply, along with any applicable section 7
additional terms, to the whole of the work, and all its parts,
regardless of how they are packaged. This License gives no
permission to license the work in any other way, but it does not
invalidate such permission if you have separately received it.
d) If the work has interactive user interfaces, each must display
Appropriate Legal Notices; however, if the Program has interactive
interfaces that do not display Appropriate Legal Notices, your
work need not make them do so.
A compilation of a covered work with other separate and independent
works, which are not by their nature extensions of the covered work,
and which are not combined with it such as to form a larger program,
in or on a volume of a storage or distribution medium, is called an
"aggregate" if the compilation and its resulting copyright are not
used to limit the access or legal rights of the compilation's users
beyond what the individual works permit. Inclusion of a covered work
in an aggregate does not cause this License to apply to the other
parts of the aggregate.
6. Conveying Non-Source Forms.
You may convey a covered work in object code form under the terms
of sections 4 and 5, provided that you also convey the
machine-readable Corresponding Source under the terms of this License,
in one of these ways:
a) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by the
Corresponding Source fixed on a durable physical medium
customarily used for software interchange.
b) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by a
written offer, valid for at least three years and valid for as
long as you offer spare parts or customer support for that product
model, to give anyone who possesses the object code either (1) a
copy of the Corresponding Source for all the software in the
product that is covered by this License, on a durable physical
medium customarily used for software interchange, for a price no
more than your reasonable cost of physically performing this
conveying of source, or (2) access to copy the
Corresponding Source from a network server at no charge.
c) Convey individual copies of the object code with a copy of the
written offer to provide the Corresponding Source. This
alternative is allowed only occasionally and noncommercially, and
only if you received the object code with such an offer, in accord
with subsection 6b.
d) Convey the object code by offering access from a designated
place (gratis or for a charge), and offer equivalent access to the
Corresponding Source in the same way through the same place at no
further charge. You need not require recipients to copy the
Corresponding Source along with the object code. If the place to
copy the object code is a network server, the Corresponding Source
may be on a different server (operated by you or a third party)
that supports equivalent copying facilities, provided you maintain
clear directions next to the object code saying where to find the
Corresponding Source. Regardless of what server hosts the
Corresponding Source, you remain obligated to ensure that it is
available for as long as needed to satisfy these requirements.
e) Convey the object code using peer-to-peer transmission, provided
you inform other peers where the object code and Corresponding
Source of the work are being offered to the general public at no
charge under subsection 6d.
A separable portion of the object code, whose source code is excluded
from the Corresponding Source as a System Library, need not be
included in conveying the object code work.
A "User Product" is either (1) a "consumer product", which means any
tangible personal property which is normally used for personal, family,
or household purposes, or (2) anything designed or sold for incorporation
into a dwelling. In determining whether a product is a consumer product,
doubtful cases shall be resolved in favor of coverage. For a particular
product received by a particular user, "normally used" refers to a
typical or common use of that class of product, regardless of the status
of the particular user or of the way in which the particular user
actually uses, or expects or is expected to use, the product. A product
is a consumer product regardless of whether the product has substantial
commercial, industrial or non-consumer uses, unless such uses represent
the only significant mode of use of the product.
"Installation Information" for a User Product means any methods,
procedures, authorization keys, or other information required to install
and execute modified versions of a covered work in that User Product from
a modified version of its Corresponding Source. The information must
suffice to ensure that the continued functioning of the modified object
code is in no case prevented or interfered with solely because
modification has been made.
If you convey an object code work under this section in, or with, or
specifically for use in, a User Product, and the conveying occurs as
part of a transaction in which the right of possession and use of the
User Product is transferred to the recipient in perpetuity or for a
fixed term (regardless of how the transaction is characterized), the
Corresponding Source conveyed under this section must be accompanied
by the Installation Information. But this requirement does not apply
if neither you nor any third party retains the ability to install
modified object code on the User Product (for example, the work has
been installed in ROM).
The requirement to provide Installation Information does not include a
requirement to continue to provide support service, warranty, or updates
for a work that has been modified or installed by the recipient, or for
the User Product in which it has been modified or installed. Access to a
network may be denied when the modification itself materially and
adversely affects the operation of the network or violates the rules and
protocols for communication across the network.
Corresponding Source conveyed, and Installation Information provided,
in accord with this section must be in a format that is publicly
documented (and with an implementation available to the public in
source code form), and must require no special password or key for
unpacking, reading or copying.
7. Additional Terms.
"Additional permissions" are terms that supplement the terms of this
License by making exceptions from one or more of its conditions.
Additional permissions that are applicable to the entire Program shall
be treated as though they were included in this License, to the extent
that they are valid under applicable law. If additional permissions
apply only to part of the Program, that part may be used separately
under those permissions, but the entire Program remains governed by
this License without regard to the additional permissions.
When you convey a copy of a covered work, you may at your option
remove any additional permissions from that copy, or from any part of
it. (Additional permissions may be written to require their own
removal in certain cases when you modify the work.) You may place
additional permissions on material, added by you to a covered work,
for which you have or can give appropriate copyright permission.
Notwithstanding any other provision of this License, for material you
add to a covered work, you may (if authorized by the copyright holders of
that material) supplement the terms of this License with terms:
a) Disclaiming warranty or limiting liability differently from the
terms of sections 15 and 16 of this License; or
b) Requiring preservation of specified reasonable legal notices or
author attributions in that material or in the Appropriate Legal
Notices displayed by works containing it; or
c) Prohibiting misrepresentation of the origin of that material, or
requiring that modified versions of such material be marked in
reasonable ways as different from the original version; or
d) Limiting the use for publicity purposes of names of licensors or
authors of the material; or
e) Declining to grant rights under trademark law for use of some
trade names, trademarks, or service marks; or
f) Requiring indemnification of licensors and authors of that
material by anyone who conveys the material (or modified versions of
it) with contractual assumptions of liability to the recipient, for
any liability that these contractual assumptions directly impose on
those licensors and authors.
All other non-permissive additional terms are considered "further
restrictions" within the meaning of section 10. If the Program as you
received it, or any part of it, contains a notice stating that it is
governed by this License along with a term that is a further
restriction, you may remove that term. If a license document contains
a further restriction but permits relicensing or conveying under this
License, you may add to a covered work material governed by the terms
of that license document, provided that the further restriction does
not survive such relicensing or conveying.
If you add terms to a covered work in accord with this section, you
must place, in the relevant source files, a statement of the
additional terms that apply to those files, or a notice indicating
where to find the applicable terms.
Additional terms, permissive or non-permissive, may be stated in the
form of a separately written license, or stated as exceptions;
the above requirements apply either way.
8. Termination.
You may not propagate or modify a covered work except as expressly
provided under this License. Any attempt otherwise to propagate or
modify it is void, and will automatically terminate your rights under
this License (including any patent licenses granted under the third
paragraph of section 11).
However, if you cease all violation of this License, then your
license from a particular copyright holder is reinstated (a)
provisionally, unless and until the copyright holder explicitly and
finally terminates your license, and (b) permanently, if the copyright
holder fails to notify you of the violation by some reasonable means
prior to 60 days after the cessation.
Moreover, your license from a particular copyright holder is
reinstated permanently if the copyright holder notifies you of the
violation by some reasonable means, this is the first time you have
received notice of violation of this License (for any work) from that
copyright holder, and you cure the violation prior to 30 days after
your receipt of the notice.
Termination of your rights under this section does not terminate the
licenses of parties who have received copies or rights from you under
this License. If your rights have been terminated and not permanently
reinstated, you do not qualify to receive new licenses for the same
material under section 10.
9. Acceptance Not Required for Having Copies.
You are not required to accept this License in order to receive or
run a copy of the Program. Ancillary propagation of a covered work
occurring solely as a consequence of using peer-to-peer transmission
to receive a copy likewise does not require acceptance. However,
nothing other than this License grants you permission to propagate or
modify any covered work. These actions infringe copyright if you do
not accept this License. Therefore, by modifying or propagating a
covered work, you indicate your acceptance of this License to do so.
10. Automatic Licensing of Downstream Recipients.
Each time you convey a covered work, the recipient automatically
receives a license from the original licensors, to run, modify and
propagate that work, subject to this License. You are not responsible
for enforcing compliance by third parties with this License.
An "entity transaction" is a transaction transferring control of an
organization, or substantially all assets of one, or subdividing an
organization, or merging organizations. If propagation of a covered
work results from an entity transaction, each party to that
transaction who receives a copy of the work also receives whatever
licenses to the work the party's predecessor in interest had or could
give under the previous paragraph, plus a right to possession of the
Corresponding Source of the work from the predecessor in interest, if
the predecessor has it or can get it with reasonable efforts.
You may not impose any further restrictions on the exercise of the
rights granted or affirmed under this License. For example, you may
not impose a license fee, royalty, or other charge for exercise of
rights granted under this License, and you may not initiate litigation
(including a cross-claim or counterclaim in a lawsuit) alleging that
any patent claim is infringed by making, using, selling, offering for
sale, or importing the Program or any portion of it.
11. Patents.
A "contributor" is a copyright holder who authorizes use under this
License of the Program or a work on which the Program is based. The
work thus licensed is called the contributor's "contributor version".
A contributor's "essential patent claims" are all patent claims
owned or controlled by the contributor, whether already acquired or
hereafter acquired, that would be infringed by some manner, permitted
by this License, of making, using, or selling its contributor version,
but do not include claims that would be infringed only as a
consequence of further modification of the contributor version. For
purposes of this definition, "control" includes the right to grant
patent sublicenses in a manner consistent with the requirements of
this License.
Each contributor grants you a non-exclusive, worldwide, royalty-free
patent license under the contributor's essential patent claims, to
make, use, sell, offer for sale, import and otherwise run, modify and
propagate the contents of its contributor version.
In the following three paragraphs, a "patent license" is any express
agreement or commitment, however denominated, not to enforce a patent
(such as an express permission to practice a patent or covenant not to
sue for patent infringement). To "grant" such a patent license to a
party means to make such an agreement or commitment not to enforce a
patent against the party.
If you convey a covered work, knowingly relying on a patent license,
and the Corresponding Source of the work is not available for anyone
to copy, free of charge and under the terms of this License, through a
publicly available network server or other readily accessible means,
then you must either (1) cause the Corresponding Source to be so
available, or (2) arrange to deprive yourself of the benefit of the
patent license for this particular work, or (3) arrange, in a manner
consistent with the requirements of this License, to extend the patent
license to downstream recipients. "Knowingly relying" means you have
actual knowledge that, but for the patent license, your conveying the
covered work in a country, or your recipient's use of the covered work
in a country, would infringe one or more identifiable patents in that
country that you have reason to believe are valid.
If, pursuant to or in connection with a single transaction or
arrangement, you convey, or propagate by procuring conveyance of, a
covered work, and grant a patent license to some of the parties
receiving the covered work authorizing them to use, propagate, modify
or convey a specific copy of the covered work, then the patent license
you grant is automatically extended to all recipients of the covered
work and works based on it.
A patent license is "discriminatory" if it does not include within
the scope of its coverage, prohibits the exercise of, or is
conditioned on the non-exercise of one or more of the rights that are
specifically granted under this License. You may not convey a covered
work if you are a party to an arrangement with a third party that is
in the business of distributing software, under which you make payment
to the third party based on the extent of your activity of conveying
the work, and under which the third party grants, to any of the
parties who would receive the covered work from you, a discriminatory
patent license (a) in connection with copies of the covered work
conveyed by you (or copies made from those copies), or (b) primarily
for and in connection with specific products or compilations that
contain the covered work, unless you entered into that arrangement,
or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting
any implied license or other defenses to infringement that may
otherwise be available to you under applicable patent law.
12. No Surrender of Others' Freedom.
If conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
excuse you from the conditions of this License. If you cannot convey a
covered work so as to satisfy simultaneously your obligations under this
License and any other pertinent obligations, then as a consequence you may
not convey it at all. For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU Affero General Public License into a single
combined work, and to convey the resulting work. The terms of this
License will continue to apply to the part which is the covered work,
but the special requirements of the GNU Affero General Public License,
section 13, concerning interaction through a network will apply to the
combination as such.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
Later license versions may give you additional or different
permissions. However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.

98
.venv/lib/python3.9/site-packages/numpy/__config__.py Normal file
View File

@@ -0,0 +1,98 @@
# This file is generated by numpy's setup.py
# It contains system_info results at the time of building this package.
__all__ = ["get_info","show"]
import os
import sys
extra_dll_dir = os.path.join(os.path.dirname(__file__), '.libs')
if sys.platform == 'win32' and os.path.isdir(extra_dll_dir):
if sys.version_info >= (3, 8):
os.add_dll_directory(extra_dll_dir)
else:
os.environ.setdefault('PATH', '')
os.environ['PATH'] += os.pathsep + extra_dll_dir
blas_mkl_info={}
blis_info={}
openblas_info={'libraries': ['openblas', 'openblas'], 'library_dirs': ['/usr/local/lib'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None)], 'runtime_library_dirs': ['/usr/local/lib']}
blas_opt_info={'libraries': ['openblas', 'openblas'], 'library_dirs': ['/usr/local/lib'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None)], 'runtime_library_dirs': ['/usr/local/lib']}
lapack_mkl_info={}
openblas_lapack_info={'libraries': ['openblas', 'openblas'], 'library_dirs': ['/usr/local/lib'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None)], 'runtime_library_dirs': ['/usr/local/lib']}
lapack_opt_info={'libraries': ['openblas', 'openblas'], 'library_dirs': ['/usr/local/lib'], 'language': 'c', 'define_macros': [('HAVE_CBLAS', None)], 'runtime_library_dirs': ['/usr/local/lib']}
def get_info(name):
g = globals()
return g.get(name, g.get(name + "_info", {}))
def show():
"""
Show libraries in the system on which NumPy was built.
Print information about various resources (libraries, library
directories, include directories, etc.) in the system on which
NumPy was built.
See Also
--------
get_include : Returns the directory containing NumPy C
header files.
Notes
-----
Classes specifying the information to be printed are defined
in the `numpy.distutils.system_info` module.
Information may include:
* ``language``: language used to write the libraries (mostly
C or f77)
* ``libraries``: names of libraries found in the system
* ``library_dirs``: directories containing the libraries
* ``include_dirs``: directories containing library header files
* ``src_dirs``: directories containing library source files
* ``define_macros``: preprocessor macros used by
``distutils.setup``
* ``baseline``: minimum CPU features required
* ``found``: dispatched features supported in the system
* ``not found``: dispatched features that are not supported
in the system
Examples
--------
>>> import numpy as np
>>> np.show_config()
blas_opt_info:
language = c
define_macros = [('HAVE_CBLAS', None)]
libraries = ['openblas', 'openblas']
library_dirs = ['/usr/local/lib']
"""
from numpy.core._multiarray_umath import (
__cpu_features__, __cpu_baseline__, __cpu_dispatch__
)
for name,info_dict in globals().items():
if name[0] == "_" or type(info_dict) is not type({}): continue
print(name + ":")
if not info_dict:
print(" NOT AVAILABLE")
for k,v in info_dict.items():
v = str(v)
if k == "sources" and len(v) > 200:
v = v[:60] + " ...\n... " + v[-60:]
print(" %s = %s" % (k,v))
features_found, features_not_found = [], []
for feature in __cpu_dispatch__:
if __cpu_features__[feature]:
features_found.append(feature)
else:
features_not_found.append(feature)
print("Supported SIMD extensions in this NumPy install:")
print(" baseline = %s" % (','.join(__cpu_baseline__)))
print(" found = %s" % (','.join(features_found)))
print(" not found = %s" % (','.join(features_not_found)))

1053
.venv/lib/python3.9/site-packages/numpy/__init__.cython-30.pxd Normal file
View File

File diff suppressed because it is too large Load Diff

1018
.venv/lib/python3.9/site-packages/numpy/__init__.pxd Normal file
View File

File diff suppressed because it is too large Load Diff

429
.venv/lib/python3.9/site-packages/numpy/__init__.py Normal file
View File

@@ -0,0 +1,429 @@
"""
NumPy
=====
Provides
1. An array object of arbitrary homogeneous items
2. Fast mathematical operations over arrays
3. Linear Algebra, Fourier Transforms, Random Number Generation
How to use the documentation
----------------------------
Documentation is available in two forms: docstrings provided
with the code, and a loose standing reference guide, available from
`the NumPy homepage <https://www.scipy.org>`_.
We recommend exploring the docstrings using
`IPython <https://ipython.org>`_, an advanced Python shell with
TAB-completion and introspection capabilities. See below for further
instructions.
The docstring examples assume that `numpy` has been imported as `np`::
>>> import numpy as np
Code snippets are indicated by three greater-than signs::
>>> x = 42
>>> x = x + 1
Use the built-in ``help`` function to view a function's docstring::
>>> help(np.sort)
... # doctest: +SKIP
For some objects, ``np.info(obj)`` may provide additional help. This is
particularly true if you see the line "Help on ufunc object:" at the top
of the help() page. Ufuncs are implemented in C, not Python, for speed.
The native Python help() does not know how to view their help, but our
np.info() function does.
To search for documents containing a keyword, do::
>>> np.lookfor('keyword')
... # doctest: +SKIP
General-purpose documents like a glossary and help on the basic concepts
of numpy are available under the ``doc`` sub-module::
>>> from numpy import doc
>>> help(doc)
... # doctest: +SKIP
Available subpackages
---------------------
doc
Topical documentation on broadcasting, indexing, etc.
lib
Basic functions used by several sub-packages.
random
Core Random Tools
linalg
Core Linear Algebra Tools
fft
Core FFT routines
polynomial
Polynomial tools
testing
NumPy testing tools
f2py
Fortran to Python Interface Generator.
distutils
Enhancements to distutils with support for
Fortran compilers support and more.
Utilities
---------
test
Run numpy unittests
show_config
Show numpy build configuration
dual
Overwrite certain functions with high-performance SciPy tools.
Note: `numpy.dual` is deprecated. Use the functions from NumPy or Scipy
directly instead of importing them from `numpy.dual`.
matlib
Make everything matrices.
__version__
NumPy version string
Viewing documentation using IPython
-----------------------------------
Start IPython with the NumPy profile (``ipython -p numpy``), which will
import `numpy` under the alias `np`. Then, use the ``cpaste`` command to
paste examples into the shell. To see which functions are available in
`numpy`, type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use
``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow
down the list. To view the docstring for a function, use
``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view
the source code).
Copies vs. in-place operation
-----------------------------
Most of the functions in `numpy` return a copy of the array argument
(e.g., `np.sort`). In-place versions of these functions are often
available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``.
Exceptions to this rule are documented.
"""
import sys
import warnings
from ._globals import (
ModuleDeprecationWarning, VisibleDeprecationWarning, _NoValue
)
# We first need to detect if we're being called as part of the numpy setup
# procedure itself in a reliable manner.
try:
__NUMPY_SETUP__
except NameError:
__NUMPY_SETUP__ = False
if __NUMPY_SETUP__:
sys.stderr.write('Running from numpy source directory.\n')
else:
try:
from numpy.__config__ import show as show_config
except ImportError as e:
msg = """Error importing numpy: you should not try to import numpy from
its source directory; please exit the numpy source tree, and relaunch
your python interpreter from there."""
raise ImportError(msg) from e
__all__ = ['ModuleDeprecationWarning',
'VisibleDeprecationWarning']
# get the version using versioneer
from ._version import get_versions
vinfo = get_versions()
__version__ = vinfo.get("closest-tag", vinfo["version"])
__git_version__ = vinfo.get("full-revisionid")
del get_versions, vinfo
# mapping of {name: (value, deprecation_msg)}
__deprecated_attrs__ = {}
# Allow distributors to run custom init code
from . import _distributor_init
from . import core
from .core import *
from . import compat
from . import lib
# NOTE: to be revisited following future namespace cleanup.
# See gh-14454 and gh-15672 for discussion.
from .lib import *
from . import linalg
from . import fft
from . import polynomial
from . import random
from . import ctypeslib
from . import ma
from . import matrixlib as _mat
from .matrixlib import *
# Deprecations introduced in NumPy 1.20.0, 2020-06-06
import builtins as _builtins
_msg = (
"`np.{n}` is a deprecated alias for the builtin `{n}`. "
"To silence this warning, use `{n}` by itself. Doing this will not "
"modify any behavior and is safe. {extended_msg}\n"
"Deprecated in NumPy 1.20; for more details and guidance: "
"https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations")
_specific_msg = (
"If you specifically wanted the numpy scalar type, use `np.{}` here.")
_int_extended_msg = (
"When replacing `np.{}`, you may wish to use e.g. `np.int64` "
"or `np.int32` to specify the precision. If you wish to review "
"your current use, check the release note link for "
"additional information.")
_type_info = [
("object", ""), # The NumPy scalar only exists by name.
("bool", _specific_msg.format("bool_")),
("float", _specific_msg.format("float64")),
("complex", _specific_msg.format("complex128")),
("str", _specific_msg.format("str_")),
("int", _int_extended_msg.format("int"))]
__deprecated_attrs__.update({
n: (getattr(_builtins, n), _msg.format(n=n, extended_msg=extended_msg))
for n, extended_msg in _type_info
})
# Numpy 1.20.0, 2020-10-19
__deprecated_attrs__["typeDict"] = (
core.numerictypes.typeDict,
"`np.typeDict` is a deprecated alias for `np.sctypeDict`."
)
_msg = (
"`np.{n}` is a deprecated alias for `np.compat.{n}`. "
"To silence this warning, use `np.compat.{n}` by itself. "
"In the likely event your code does not need to work on Python 2 "
"you can use the builtin `{n2}` for which `np.compat.{n}` is itself "
"an alias. Doing this will not modify any behaviour and is safe. "
"{extended_msg}\n"
"Deprecated in NumPy 1.20; for more details and guidance: "
"https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations")
__deprecated_attrs__["long"] = (
getattr(compat, "long"),
_msg.format(n="long", n2="int",
extended_msg=_int_extended_msg.format("long")))
__deprecated_attrs__["unicode"] = (
getattr(compat, "unicode"),
_msg.format(n="unicode", n2="str",
extended_msg=_specific_msg.format("str_")))
del _msg, _specific_msg, _int_extended_msg, _type_info, _builtins
from .core import round, abs, max, min
# now that numpy modules are imported, can initialize limits
core.getlimits._register_known_types()
__all__.extend(['__version__', 'show_config'])
__all__.extend(core.__all__)
__all__.extend(_mat.__all__)
__all__.extend(lib.__all__)
__all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma'])
# These are exported by np.core, but are replaced by the builtins below
# remove them to ensure that we don't end up with `np.long == np.int_`,
# which would be a breaking change.
del long, unicode
__all__.remove('long')
__all__.remove('unicode')
# Remove things that are in the numpy.lib but not in the numpy namespace
# Note that there is a test (numpy/tests/test_public_api.py:test_numpy_namespace)
# that prevents adding more things to the main namespace by accident.
# The list below will grow until the `from .lib import *` fixme above is
# taken care of
__all__.remove('Arrayterator')
del Arrayterator
# These names were removed in NumPy 1.20. For at least one release,
# attempts to access these names in the numpy namespace will trigger
# a warning, and calling the function will raise an exception.
_financial_names = ['fv', 'ipmt', 'irr', 'mirr', 'nper', 'npv', 'pmt',
'ppmt', 'pv', 'rate']
__expired_functions__ = {
name: (f'In accordance with NEP 32, the function {name} was removed '
'from NumPy version 1.20. A replacement for this function '
'is available in the numpy_financial library: '
'https://pypi.org/project/numpy-financial')
for name in _financial_names}
# Filter out Cython harmless warnings
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
# oldnumeric and numarray were removed in 1.9. In case some packages import
# but do not use them, we define them here for backward compatibility.
oldnumeric = 'removed'
numarray = 'removed'
if sys.version_info[:2] >= (3, 7):
# module level getattr is only supported in 3.7 onwards
# https://www.python.org/dev/peps/pep-0562/
def __getattr__(attr):
# Warn for expired attributes, and return a dummy function
# that always raises an exception.
try:
msg = __expired_functions__[attr]
except KeyError:
pass
else:
warnings.warn(msg, DeprecationWarning, stacklevel=2)
def _expired(*args, **kwds):
raise RuntimeError(msg)
return _expired
# Emit warnings for deprecated attributes
try:
val, msg = __deprecated_attrs__[attr]
except KeyError:
pass
else:
warnings.warn(msg, DeprecationWarning, stacklevel=2)
return val
# Importing Tester requires importing all of UnitTest which is not a
# cheap import Since it is mainly used in test suits, we lazy import it
# here to save on the order of 10 ms of import time for most users
#
# The previous way Tester was imported also had a side effect of adding
# the full `numpy.testing` namespace
if attr == 'testing':
import numpy.testing as testing
return testing
elif attr == 'Tester':
from .testing import Tester
return Tester
raise AttributeError("module {!r} has no attribute "
"{!r}".format(__name__, attr))
def __dir__():
return list(globals().keys() | {'Tester', 'testing'})
else:
# We don't actually use this ourselves anymore, but I'm not 100% sure that
# no-one else in the world is using it (though I hope not)
from .testing import Tester
# We weren't able to emit a warning about these, so keep them around
globals().update({
k: v
for k, (v, msg) in __deprecated_attrs__.items()
})
# Pytest testing
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester
def _sanity_check():
"""
Quick sanity checks for common bugs caused by environment.
There are some cases e.g. with wrong BLAS ABI that cause wrong
results under specific runtime conditions that are not necessarily
achieved during test suite runs, and it is useful to catch those early.
See https://github.com/numpy/numpy/issues/8577 and other
similar bug reports.
"""
try:
x = ones(2, dtype=float32)
if not abs(x.dot(x) - 2.0) < 1e-5:
raise AssertionError()
except AssertionError:
msg = ("The current Numpy installation ({!r}) fails to "
"pass simple sanity checks. This can be caused for example "
"by incorrect BLAS library being linked in, or by mixing "
"package managers (pip, conda, apt, ...). Search closed "
"numpy issues for similar problems.")
raise RuntimeError(msg.format(__file__)) from None
_sanity_check()
del _sanity_check
def _mac_os_check():
"""
Quick Sanity check for Mac OS look for accelerate build bugs.
Testing numpy polyfit calls init_dgelsd(LAPACK)
"""
try:
c = array([3., 2., 1.])
x = linspace(0, 2, 5)
y = polyval(c, x)
_ = polyfit(x, y, 2, cov=True)
except ValueError:
pass
import sys
if sys.platform == "darwin":
with warnings.catch_warnings(record=True) as w:
_mac_os_check()
# Throw runtime error, if the test failed Check for warning and error_message
error_message = ""
if len(w) > 0:
error_message = "{}: {}".format(w[-1].category.__name__, str(w[-1].message))
msg = (
"Polyfit sanity test emitted a warning, most likely due "
"to using a buggy Accelerate backend. If you compiled "
"yourself, more information is available at "
"https://numpy.org/doc/stable/user/building.html#accelerated-blas-lapack-libraries "
"Otherwise report this to the vendor "
"that provided NumPy.\n{}\n".format(error_message))
raise RuntimeError(msg)
del _mac_os_check
# We usually use madvise hugepages support, but on some old kernels it
# is slow and thus better avoided.
# Specifically kernel version 4.6 had a bug fix which probably fixed this:
# https://github.com/torvalds/linux/commit/7cf91a98e607c2f935dbcc177d70011e95b8faff
import os
use_hugepage = os.environ.get("NUMPY_MADVISE_HUGEPAGE", None)
if sys.platform == "linux" and use_hugepage is None:
# If there is an issue with parsing the kernel version,
# set use_hugepages to 0. Usage of LooseVersion will handle
# the kernel version parsing better, but avoided since it
# will increase the import time. See: #16679 for related discussion.
try:
use_hugepage = 1
kernel_version = os.uname().release.split(".")[:2]
kernel_version = tuple(int(v) for v in kernel_version)
if kernel_version < (4, 6):
use_hugepage = 0
except ValueError:
use_hugepages = 0
elif use_hugepage is None:
# This is not Linux, so it should not matter, just enable anyway
use_hugepage = 1
else:
use_hugepage = int(use_hugepage)
# Note that this will currently only make a difference on Linux
core.multiarray._set_madvise_hugepage(use_hugepage)
# Give a warning if NumPy is reloaded or imported on a sub-interpreter
# We do this from python, since the C-module may not be reloaded and
# it is tidier organized.
core.multiarray._multiarray_umath._reload_guard()
from ._version import get_versions
__version__ = get_versions()['version']
del get_versions

3741
.venv/lib/python3.9/site-packages/numpy/__init__.pyi Normal file
View File

File diff suppressed because it is too large Load Diff

BIN
.venv/lib/python3.9/site-packages/numpy/__pycache__/__config__.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/__pycache__/__init__.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/__pycache__/_distributor_init.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/__pycache__/_globals.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/__pycache__/_pytesttester.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/__pycache__/_version.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/__pycache__/conftest.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/__pycache__/ctypeslib.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/__pycache__/dual.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/__pycache__/matlib.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/__pycache__/setup.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/__pycache__/version.cpython-39.pyc Normal file
View File

Binary file not shown.

10
.venv/lib/python3.9/site-packages/numpy/_distributor_init.py Normal file
View File

@@ -0,0 +1,10 @@
""" Distributor init file
Distributors: you can add custom code here to support particular distributions
of numpy.
For example, this is a good place to put any checks for hardware requirements.
The numpy standard source distribution will not put code in this file, so you
can safely replace this file with your own version.
"""

91
.venv/lib/python3.9/site-packages/numpy/_globals.py Normal file
View File

@@ -0,0 +1,91 @@
"""
Module defining global singleton classes.
This module raises a RuntimeError if an attempt to reload it is made. In that
way the identities of the classes defined here are fixed and will remain so
even if numpy itself is reloaded. In particular, a function like the following
will still work correctly after numpy is reloaded::
def foo(arg=np._NoValue):
if arg is np._NoValue:
...
That was not the case when the singleton classes were defined in the numpy
``__init__.py`` file. See gh-7844 for a discussion of the reload problem that
motivated this module.
"""
__ALL__ = [
'ModuleDeprecationWarning', 'VisibleDeprecationWarning', '_NoValue'
]
# Disallow reloading this module so as to preserve the identities of the
# classes defined here.
if '_is_loaded' in globals():
raise RuntimeError('Reloading numpy._globals is not allowed')
_is_loaded = True
class ModuleDeprecationWarning(DeprecationWarning):
"""Module deprecation warning.
The nose tester turns ordinary Deprecation warnings into test failures.
That makes it hard to deprecate whole modules, because they get
imported by default. So this is a special Deprecation warning that the
nose tester will let pass without making tests fail.
"""
ModuleDeprecationWarning.__module__ = 'numpy'
class VisibleDeprecationWarning(UserWarning):
"""Visible deprecation warning.
By default, python will not show deprecation warnings, so this class
can be used when a very visible warning is helpful, for example because
the usage is most likely a user bug.
"""
VisibleDeprecationWarning.__module__ = 'numpy'
class _NoValueType:
"""Special keyword value.
The instance of this class may be used as the default value assigned to a
keyword if no other obvious default (e.g., `None`) is suitable,
Common reasons for using this keyword are:
- A new keyword is added to a function, and that function forwards its
inputs to another function or method which can be defined outside of
NumPy. For example, ``np.std(x)`` calls ``x.std``, so when a ``keepdims``
keyword was added that could only be forwarded if the user explicitly
specified ``keepdims``; downstream array libraries may not have added
the same keyword, so adding ``x.std(..., keepdims=keepdims)``
unconditionally could have broken previously working code.
- A keyword is being deprecated, and a deprecation warning must only be
emitted when the keyword is used.
"""
__instance = None
def __new__(cls):
# ensure that only one instance exists
if not cls.__instance:
cls.__instance = super().__new__(cls)
return cls.__instance
# needed for python 2 to preserve identity through a pickle
def __reduce__(self):
return (self.__class__, ())
def __repr__(self):
return "<no value>"
_NoValue = _NoValueType()

201
.venv/lib/python3.9/site-packages/numpy/_pytesttester.py Normal file
View File

@@ -0,0 +1,201 @@
"""
Pytest test running.
This module implements the ``test()`` function for NumPy modules. The usual
boiler plate for doing that is to put the following in the module
``__init__.py`` file::
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester
Warnings filtering and other runtime settings should be dealt with in the
``pytest.ini`` file in the numpy repo root. The behavior of the test depends on
whether or not that file is found as follows:
* ``pytest.ini`` is present (develop mode)
All warnings except those explicitly filtered out are raised as error.
* ``pytest.ini`` is absent (release mode)
DeprecationWarnings and PendingDeprecationWarnings are ignored, other
warnings are passed through.
In practice, tests run from the numpy repo are run in develop mode. That
includes the standard ``python runtests.py`` invocation.
This module is imported by every numpy subpackage, so lies at the top level to
simplify circular import issues. For the same reason, it contains no numpy
imports at module scope, instead importing numpy within function calls.
"""
import sys
import os
__all__ = ['PytestTester']
def _show_numpy_info():
import numpy as np
print("NumPy version %s" % np.__version__)
relaxed_strides = np.ones((10, 1), order="C").flags.f_contiguous
print("NumPy relaxed strides checking option:", relaxed_strides)
info = np.lib.utils._opt_info()
print("NumPy CPU features: ", (info if info else 'nothing enabled'))
class PytestTester:
"""
Pytest test runner.
A test function is typically added to a package's __init__.py like so::
from numpy._pytesttester import PytestTester
test = PytestTester(__name__).test
del PytestTester
Calling this test function finds and runs all tests associated with the
module and all its sub-modules.
Attributes
----------
module_name : str
Full path to the package to test.
Parameters
----------
module_name : module name
The name of the module to test.
Notes
-----
Unlike the previous ``nose``-based implementation, this class is not
publicly exposed as it performs some ``numpy``-specific warning
suppression.
"""
def __init__(self, module_name):
self.module_name = module_name
def __call__(self, label='fast', verbose=1, extra_argv=None,
doctests=False, coverage=False, durations=-1, tests=None):
"""
Run tests for module using pytest.
Parameters
----------
label : {'fast', 'full'}, optional
Identifies the tests to run. When set to 'fast', tests decorated
with `pytest.mark.slow` are skipped, when 'full', the slow marker
is ignored.
verbose : int, optional
Verbosity value for test outputs, in the range 1-3. Default is 1.
extra_argv : list, optional
List with any extra arguments to pass to pytests.
doctests : bool, optional
.. note:: Not supported
coverage : bool, optional
If True, report coverage of NumPy code. Default is False.
Requires installation of (pip) pytest-cov.
durations : int, optional
If < 0, do nothing, If 0, report time of all tests, if > 0,
report the time of the slowest `timer` tests. Default is -1.
tests : test or list of tests
Tests to be executed with pytest '--pyargs'
Returns
-------
result : bool
Return True on success, false otherwise.
Notes
-----
Each NumPy module exposes `test` in its namespace to run all tests for
it. For example, to run all tests for numpy.lib:
>>> np.lib.test() #doctest: +SKIP
Examples
--------
>>> result = np.lib.test() #doctest: +SKIP
...
1023 passed, 2 skipped, 6 deselected, 1 xfailed in 10.39 seconds
>>> result
True
"""
import pytest
import warnings
module = sys.modules[self.module_name]
module_path = os.path.abspath(module.__path__[0])
# setup the pytest arguments
pytest_args = ["-l"]
# offset verbosity. The "-q" cancels a "-v".
pytest_args += ["-q"]
# Filter out distutils cpu warnings (could be localized to
# distutils tests). ASV has problems with top level import,
# so fetch module for suppression here.
with warnings.catch_warnings():
warnings.simplefilter("always")
from numpy.distutils import cpuinfo
# Filter out annoying import messages. Want these in both develop and
# release mode.
pytest_args += [
"-W ignore:Not importing directory",
"-W ignore:numpy.dtype size changed",
"-W ignore:numpy.ufunc size changed",
"-W ignore::UserWarning:cpuinfo",
]
# When testing matrices, ignore their PendingDeprecationWarnings
pytest_args += [
"-W ignore:the matrix subclass is not",
"-W ignore:Importing from numpy.matlib is",
]
if doctests:
raise ValueError("Doctests not supported")
if extra_argv:
pytest_args += list(extra_argv)
if verbose > 1:
pytest_args += ["-" + "v"*(verbose - 1)]
if coverage:
pytest_args += ["--cov=" + module_path]
if label == "fast":
# not importing at the top level to avoid circular import of module
from numpy.testing import IS_PYPY
if IS_PYPY:
pytest_args += ["-m", "not slow and not slow_pypy"]
else:
pytest_args += ["-m", "not slow"]
elif label != "full":
pytest_args += ["-m", label]
if durations >= 0:
pytest_args += ["--durations=%s" % durations]
if tests is None:
tests = [self.module_name]
pytest_args += ["--pyargs"] + list(tests)
# run tests.
_show_numpy_info()
try:
code = pytest.main(pytest_args)
except SystemExit as exc:
code = exc.code
return code == 0

21
.venv/lib/python3.9/site-packages/numpy/_version.py Normal file
View File

@@ -0,0 +1,21 @@
# This file was generated by 'versioneer.py' (0.19) from
# revision-control system data, or from the parent directory name of an
# unpacked source archive. Distribution tarballs contain a pre-generated copy
# of this file.
import json
version_json = '''
{
"date": "2021-08-15T12:15:47-0600",
"dirty": false,
"error": null,
"full-revisionid": "2fe48d2d98a85c8ea3f3d5caffd952ea69e99335",
"version": "1.21.2"
}
''' # END VERSION_JSON
def get_versions():
return json.loads(version_json)

59
.venv/lib/python3.9/site-packages/numpy/char.pyi Normal file
View File

@@ -0,0 +1,59 @@
from typing import Any, List
from numpy import (
chararray as chararray,
)
__all__: List[str]
def equal(x1, x2): ...
def not_equal(x1, x2): ...
def greater_equal(x1, x2): ...
def less_equal(x1, x2): ...
def greater(x1, x2): ...
def less(x1, x2): ...
def str_len(a): ...
def add(x1, x2): ...
def multiply(a, i): ...
def mod(a, values): ...
def capitalize(a): ...
def center(a, width, fillchar=...): ...
def count(a, sub, start=..., end=...): ...
def decode(a, encoding=..., errors=...): ...
def encode(a, encoding=..., errors=...): ...
def endswith(a, suffix, start=..., end=...): ...
def expandtabs(a, tabsize=...): ...
def find(a, sub, start=..., end=...): ...
def index(a, sub, start=..., end=...): ...
def isalnum(a): ...
def isalpha(a): ...
def isdigit(a): ...
def islower(a): ...
def isspace(a): ...
def istitle(a): ...
def isupper(a): ...
def join(sep, seq): ...
def ljust(a, width, fillchar=...): ...
def lower(a): ...
def lstrip(a, chars=...): ...
def partition(a, sep): ...
def replace(a, old, new, count=...): ...
def rfind(a, sub, start=..., end=...): ...
def rindex(a, sub, start=..., end=...): ...
def rjust(a, width, fillchar=...): ...
def rpartition(a, sep): ...
def rsplit(a, sep=..., maxsplit=...): ...
def rstrip(a, chars=...): ...
def split(a, sep=..., maxsplit=...): ...
def splitlines(a, keepends=...): ...
def startswith(a, prefix, start=..., end=...): ...
def strip(a, chars=...): ...
def swapcase(a): ...
def title(a): ...
def translate(a, table, deletechars=...): ...
def upper(a): ...
def zfill(a, width): ...
def isnumeric(a): ...
def isdecimal(a): ...
def array(obj, itemsize=..., copy=..., unicode=..., order=...): ...
def asarray(obj, itemsize=..., unicode=..., order=...): ...

18
.venv/lib/python3.9/site-packages/numpy/compat/__init__.py Normal file
View File

@@ -0,0 +1,18 @@
"""
Compatibility module.
This module contains duplicated code from Python itself or 3rd party
extensions, which may be included for the following reasons:
* compatibility
* we may only need a small subset of the copied library/module
"""
from . import _inspect
from . import py3k
from ._inspect import getargspec, formatargspec
from .py3k import *
__all__ = []
__all__.extend(_inspect.__all__)
__all__.extend(py3k.__all__)

BIN
.venv/lib/python3.9/site-packages/numpy/compat/__pycache__/__init__.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/compat/__pycache__/_inspect.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/compat/__pycache__/py3k.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/compat/__pycache__/setup.cpython-39.pyc Normal file
View File

Binary file not shown.

191
.venv/lib/python3.9/site-packages/numpy/compat/_inspect.py Normal file
View File

@@ -0,0 +1,191 @@
"""Subset of inspect module from upstream python
We use this instead of upstream because upstream inspect is slow to import, and
significantly contributes to numpy import times. Importing this copy has almost
no overhead.
"""
import types
__all__ = ['getargspec', 'formatargspec']
# ----------------------------------------------------------- type-checking
def ismethod(object):
"""Return true if the object is an instance method.
Instance method objects provide these attributes:
__doc__ documentation string
__name__ name with which this method was defined
im_class class object in which this method belongs
im_func function object containing implementation of method
im_self instance to which this method is bound, or None
"""
return isinstance(object, types.MethodType)
def isfunction(object):
"""Return true if the object is a user-defined function.
Function objects provide these attributes:
__doc__ documentation string
__name__ name with which this function was defined
func_code code object containing compiled function bytecode
func_defaults tuple of any default values for arguments
func_doc (same as __doc__)
func_globals global namespace in which this function was defined
func_name (same as __name__)
"""
return isinstance(object, types.FunctionType)
def iscode(object):
"""Return true if the object is a code object.
Code objects provide these attributes:
co_argcount number of arguments (not including * or ** args)
co_code string of raw compiled bytecode
co_consts tuple of constants used in the bytecode
co_filename name of file in which this code object was created
co_firstlineno number of first line in Python source code
co_flags bitmap: 1=optimized | 2=newlocals | 4=*arg | 8=**arg
co_lnotab encoded mapping of line numbers to bytecode indices
co_name name with which this code object was defined
co_names tuple of names of local variables
co_nlocals number of local variables
co_stacksize virtual machine stack space required
co_varnames tuple of names of arguments and local variables
"""
return isinstance(object, types.CodeType)
# ------------------------------------------------ argument list extraction
# These constants are from Python's compile.h.
CO_OPTIMIZED, CO_NEWLOCALS, CO_VARARGS, CO_VARKEYWORDS = 1, 2, 4, 8
def getargs(co):
"""Get information about the arguments accepted by a code object.
Three things are returned: (args, varargs, varkw), where 'args' is
a list of argument names (possibly containing nested lists), and
'varargs' and 'varkw' are the names of the * and ** arguments or None.
"""
if not iscode(co):
raise TypeError('arg is not a code object')
nargs = co.co_argcount
names = co.co_varnames
args = list(names[:nargs])
# The following acrobatics are for anonymous (tuple) arguments.
# Which we do not need to support, so remove to avoid importing
# the dis module.
for i in range(nargs):
if args[i][:1] in ['', '.']:
raise TypeError("tuple function arguments are not supported")
varargs = None
if co.co_flags & CO_VARARGS:
varargs = co.co_varnames[nargs]
nargs = nargs + 1
varkw = None
if co.co_flags & CO_VARKEYWORDS:
varkw = co.co_varnames[nargs]
return args, varargs, varkw
def getargspec(func):
"""Get the names and default values of a function's arguments.
A tuple of four things is returned: (args, varargs, varkw, defaults).
'args' is a list of the argument names (it may contain nested lists).
'varargs' and 'varkw' are the names of the * and ** arguments or None.
'defaults' is an n-tuple of the default values of the last n arguments.
"""
if ismethod(func):
func = func.__func__
if not isfunction(func):
raise TypeError('arg is not a Python function')
args, varargs, varkw = getargs(func.__code__)
return args, varargs, varkw, func.__defaults__
def getargvalues(frame):
"""Get information about arguments passed into a particular frame.
A tuple of four things is returned: (args, varargs, varkw, locals).
'args' is a list of the argument names (it may contain nested lists).
'varargs' and 'varkw' are the names of the * and ** arguments or None.
'locals' is the locals dictionary of the given frame.
"""
args, varargs, varkw = getargs(frame.f_code)
return args, varargs, varkw, frame.f_locals
def joinseq(seq):
if len(seq) == 1:
return '(' + seq[0] + ',)'
else:
return '(' + ', '.join(seq) + ')'
def strseq(object, convert, join=joinseq):
"""Recursively walk a sequence, stringifying each element.
"""
if type(object) in [list, tuple]:
return join([strseq(_o, convert, join) for _o in object])
else:
return convert(object)
def formatargspec(args, varargs=None, varkw=None, defaults=None,
formatarg=str,
formatvarargs=lambda name: '*' + name,
formatvarkw=lambda name: '**' + name,
formatvalue=lambda value: '=' + repr(value),
join=joinseq):
"""Format an argument spec from the 4 values returned by getargspec.
The first four arguments are (args, varargs, varkw, defaults). The
other four arguments are the corresponding optional formatting functions
that are called to turn names and values into strings. The ninth
argument is an optional function to format the sequence of arguments.
"""
specs = []
if defaults:
firstdefault = len(args) - len(defaults)
for i in range(len(args)):
spec = strseq(args[i], formatarg, join)
if defaults and i >= firstdefault:
spec = spec + formatvalue(defaults[i - firstdefault])
specs.append(spec)
if varargs is not None:
specs.append(formatvarargs(varargs))
if varkw is not None:
specs.append(formatvarkw(varkw))
return '(' + ', '.join(specs) + ')'
def formatargvalues(args, varargs, varkw, locals,
formatarg=str,
formatvarargs=lambda name: '*' + name,
formatvarkw=lambda name: '**' + name,
formatvalue=lambda value: '=' + repr(value),
join=joinseq):
"""Format an argument spec from the 4 values returned by getargvalues.
The first four arguments are (args, varargs, varkw, locals). The
next four arguments are the corresponding optional formatting functions
that are called to turn names and values into strings. The ninth
argument is an optional function to format the sequence of arguments.
"""
def convert(name, locals=locals,
formatarg=formatarg, formatvalue=formatvalue):
return formatarg(name) + formatvalue(locals[name])
specs = [strseq(arg, convert, join) for arg in args]
if varargs:
specs.append(formatvarargs(varargs) + formatvalue(locals[varargs]))
if varkw:
specs.append(formatvarkw(varkw) + formatvalue(locals[varkw]))
return '(' + ', '.join(specs) + ')'

139
.venv/lib/python3.9/site-packages/numpy/compat/py3k.py Normal file
View File

@@ -0,0 +1,139 @@
"""
Python 3.X compatibility tools.
While this file was originally intended for Python 2 -> 3 transition,
it is now used to create a compatibility layer between different
minor versions of Python 3.
While the active version of numpy may not support a given version of python, we
allow downstream libraries to continue to use these shims for forward
compatibility with numpy while they transition their code to newer versions of
Python.
"""
__all__ = ['bytes', 'asbytes', 'isfileobj', 'getexception', 'strchar',
'unicode', 'asunicode', 'asbytes_nested', 'asunicode_nested',
'asstr', 'open_latin1', 'long', 'basestring', 'sixu',
'integer_types', 'is_pathlib_path', 'npy_load_module', 'Path',
'pickle', 'contextlib_nullcontext', 'os_fspath', 'os_PathLike']
import sys
import os
from pathlib import Path
import io
import abc
from abc import ABC as abc_ABC
try:
import pickle5 as pickle
except ImportError:
import pickle
long = int
integer_types = (int,)
basestring = str
unicode = str
bytes = bytes
def asunicode(s):
if isinstance(s, bytes):
return s.decode('latin1')
return str(s)
def asbytes(s):
if isinstance(s, bytes):
return s
return str(s).encode('latin1')
def asstr(s):
if isinstance(s, bytes):
return s.decode('latin1')
return str(s)
def isfileobj(f):
return isinstance(f, (io.FileIO, io.BufferedReader, io.BufferedWriter))
def open_latin1(filename, mode='r'):
return open(filename, mode=mode, encoding='iso-8859-1')
def sixu(s):
return s
strchar = 'U'
def getexception():
return sys.exc_info()[1]
def asbytes_nested(x):
if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
return [asbytes_nested(y) for y in x]
else:
return asbytes(x)
def asunicode_nested(x):
if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
return [asunicode_nested(y) for y in x]
else:
return asunicode(x)
def is_pathlib_path(obj):
"""
Check whether obj is a `pathlib.Path` object.
Prefer using ``isinstance(obj, os.PathLike)`` instead of this function.
"""
return isinstance(obj, Path)
# from Python 3.7
class contextlib_nullcontext:
"""Context manager that does no additional processing.
Used as a stand-in for a normal context manager, when a particular
block of code is only sometimes used with a normal context manager:
cm = optional_cm if condition else nullcontext()
with cm:
# Perform operation, using optional_cm if condition is True
.. note::
Prefer using `contextlib.nullcontext` instead of this context manager.
"""
def __init__(self, enter_result=None):
self.enter_result = enter_result
def __enter__(self):
return self.enter_result
def __exit__(self, *excinfo):
pass
def npy_load_module(name, fn, info=None):
"""
Load a module.
.. versionadded:: 1.11.2
Parameters
----------
name : str
Full module name.
fn : str
Path to module file.
info : tuple, optional
Only here for backward compatibility with Python 2.*.
Returns
-------
mod : module
"""
# Explicitly lazy import this to avoid paying the cost
# of importing importlib at startup
from importlib.machinery import SourceFileLoader
return SourceFileLoader(name, fn).load_module()
os_fspath = os.fspath
os_PathLike = os.PathLike

10
.venv/lib/python3.9/site-packages/numpy/compat/setup.py Normal file
View File

@@ -0,0 +1,10 @@
def configuration(parent_package='',top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('compat', parent_package, top_path)
config.add_subpackage('tests')
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(configuration=configuration)

0
.venv/lib/python3.9/site-packages/numpy/compat/tests/__init__.py Normal file
View File

BIN
.venv/lib/python3.9/site-packages/numpy/compat/tests/__pycache__/__init__.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/compat/tests/__pycache__/test_compat.cpython-39.pyc Normal file
View File

Binary file not shown.

19
.venv/lib/python3.9/site-packages/numpy/compat/tests/test_compat.py Normal file
View File

@@ -0,0 +1,19 @@
from os.path import join
from numpy.compat import isfileobj
from numpy.testing import assert_
from numpy.testing import tempdir
def test_isfileobj():
with tempdir(prefix="numpy_test_compat_") as folder:
filename = join(folder, 'a.bin')
with open(filename, 'wb') as f:
assert_(isfileobj(f))
with open(filename, 'ab') as f:
assert_(isfileobj(f))
with open(filename, 'rb') as f:
assert_(isfileobj(f))

119
.venv/lib/python3.9/site-packages/numpy/conftest.py Normal file
View File

@@ -0,0 +1,119 @@
"""
Pytest configuration and fixtures for the Numpy test suite.
"""
import os
import tempfile
import hypothesis
import pytest
import numpy
from numpy.core._multiarray_tests import get_fpu_mode
_old_fpu_mode = None
_collect_results = {}
# Use a known and persistent tmpdir for hypothesis' caches, which
# can be automatically cleared by the OS or user.
hypothesis.configuration.set_hypothesis_home_dir(
os.path.join(tempfile.gettempdir(), ".hypothesis")
)
# We register two custom profiles for Numpy - for details see
# https://hypothesis.readthedocs.io/en/latest/settings.html
# The first is designed for our own CI runs; the latter also
# forces determinism and is designed for use via np.test()
hypothesis.settings.register_profile(
name="numpy-profile", deadline=None, print_blob=True,
)
hypothesis.settings.register_profile(
name="np.test() profile",
deadline=None, print_blob=True, database=None, derandomize=True,
suppress_health_check=hypothesis.HealthCheck.all(),
)
# Note that the default profile is chosen based on the presence
# of pytest.ini, but can be overriden by passing the
# --hypothesis-profile=NAME argument to pytest.
_pytest_ini = os.path.join(os.path.dirname(__file__), "..", "pytest.ini")
hypothesis.settings.load_profile(
"numpy-profile" if os.path.isfile(_pytest_ini) else "np.test() profile"
)
def pytest_configure(config):
config.addinivalue_line("markers",
"valgrind_error: Tests that are known to error under valgrind.")
config.addinivalue_line("markers",
"leaks_references: Tests that are known to leak references.")
config.addinivalue_line("markers",
"slow: Tests that are very slow.")
config.addinivalue_line("markers",
"slow_pypy: Tests that are very slow on pypy.")
def pytest_addoption(parser):
parser.addoption("--available-memory", action="store", default=None,
help=("Set amount of memory available for running the "
"test suite. This can result to tests requiring "
"especially large amounts of memory to be skipped. "
"Equivalent to setting environment variable "
"NPY_AVAILABLE_MEM. Default: determined"
"automatically."))
def pytest_sessionstart(session):
available_mem = session.config.getoption('available_memory')
if available_mem is not None:
os.environ['NPY_AVAILABLE_MEM'] = available_mem
#FIXME when yield tests are gone.
@pytest.hookimpl()
def pytest_itemcollected(item):
"""
Check FPU precision mode was not changed during test collection.
The clumsy way we do it here is mainly necessary because numpy
still uses yield tests, which can execute code at test collection
time.
"""
global _old_fpu_mode
mode = get_fpu_mode()
if _old_fpu_mode is None:
_old_fpu_mode = mode
elif mode != _old_fpu_mode:
_collect_results[item] = (_old_fpu_mode, mode)
_old_fpu_mode = mode
@pytest.fixture(scope="function", autouse=True)
def check_fpu_mode(request):
"""
Check FPU precision mode was not changed during the test.
"""
old_mode = get_fpu_mode()
yield
new_mode = get_fpu_mode()
if old_mode != new_mode:
raise AssertionError("FPU precision mode changed from {0:#x} to {1:#x}"
" during the test".format(old_mode, new_mode))
collect_result = _collect_results.get(request.node)
if collect_result is not None:
old_mode, new_mode = collect_result
raise AssertionError("FPU precision mode changed from {0:#x} to {1:#x}"
" when collecting the test".format(old_mode,
new_mode))
@pytest.fixture(autouse=True)
def add_np(doctest_namespace):
doctest_namespace['np'] = numpy
@pytest.fixture(autouse=True)
def env_setup(monkeypatch):
monkeypatch.setenv('PYTHONHASHSEED', '0')

166
.venv/lib/python3.9/site-packages/numpy/core/__init__.py Normal file
View File

@@ -0,0 +1,166 @@
"""
Contains the core of NumPy: ndarray, ufuncs, dtypes, etc.
Please note that this module is private. All functions and objects
are available in the main ``numpy`` namespace - use that instead.
"""
from numpy.version import version as __version__
import os
# disables OpenBLAS affinity setting of the main thread that limits
# python threads or processes to one core
env_added = []
for envkey in ['OPENBLAS_MAIN_FREE', 'GOTOBLAS_MAIN_FREE']:
if envkey not in os.environ:
os.environ[envkey] = '1'
env_added.append(envkey)
try:
from . import multiarray
except ImportError as exc:
import sys
msg = """
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
Importing the numpy C-extensions failed. This error can happen for
many reasons, often due to issues with your setup or how NumPy was
installed.
We have compiled some common reasons and troubleshooting tips at:
https://numpy.org/devdocs/user/troubleshooting-importerror.html
Please note and check the following:
* The Python version is: Python%d.%d from "%s"
* The NumPy version is: "%s"
and make sure that they are the versions you expect.
Please carefully study the documentation linked above for further help.
Original error was: %s
""" % (sys.version_info[0], sys.version_info[1], sys.executable,
__version__, exc)
raise ImportError(msg)
finally:
for envkey in env_added:
del os.environ[envkey]
del envkey
del env_added
del os
from . import umath
# Check that multiarray,umath are pure python modules wrapping
# _multiarray_umath and not either of the old c-extension modules
if not (hasattr(multiarray, '_multiarray_umath') and
hasattr(umath, '_multiarray_umath')):
import sys
path = sys.modules['numpy'].__path__
msg = ("Something is wrong with the numpy installation. "
"While importing we detected an older version of "
"numpy in {}. One method of fixing this is to repeatedly uninstall "
"numpy until none is found, then reinstall this version.")
raise ImportError(msg.format(path))
from . import numerictypes as nt
multiarray.set_typeDict(nt.sctypeDict)
from . import numeric
from .numeric import *
from . import fromnumeric
from .fromnumeric import *
from . import defchararray as char
from . import records as rec
from .records import record, recarray, format_parser
from .memmap import *
from .defchararray import chararray
from . import function_base
from .function_base import *
from . import machar
from .machar import *
from . import getlimits
from .getlimits import *
from . import shape_base
from .shape_base import *
from . import einsumfunc
from .einsumfunc import *
del nt
from .fromnumeric import amax as max, amin as min, round_ as round
from .numeric import absolute as abs
# do this after everything else, to minimize the chance of this misleadingly
# appearing in an import-time traceback
from . import _add_newdocs
from . import _add_newdocs_scalars
# add these for module-freeze analysis (like PyInstaller)
from . import _dtype_ctypes
from . import _internal
from . import _dtype
from . import _methods
__all__ = ['char', 'rec', 'memmap']
__all__ += numeric.__all__
__all__ += fromnumeric.__all__
__all__ += ['record', 'recarray', 'format_parser']
__all__ += ['chararray']
__all__ += function_base.__all__
__all__ += machar.__all__
__all__ += getlimits.__all__
__all__ += shape_base.__all__
__all__ += einsumfunc.__all__
# We used to use `np.core._ufunc_reconstruct` to unpickle. This is unnecessary,
# but old pickles saved before 1.20 will be using it, and there is no reason
# to break loading them.
def _ufunc_reconstruct(module, name):
# The `fromlist` kwarg is required to ensure that `mod` points to the
# inner-most module rather than the parent package when module name is
# nested. This makes it possible to pickle non-toplevel ufuncs such as
# scipy.special.expit for instance.
mod = __import__(module, fromlist=[name])
return getattr(mod, name)
def _ufunc_reduce(func):
# Report the `__name__`. pickle will try to find the module. Note that
# pickle supports for this `__name__` to be a `__qualname__`. It may
# make sense to add a `__qualname__` to ufuncs, to allow this more
# explicitly (Numba has ufuncs as attributes).
# See also: https://github.com/dask/distributed/issues/3450
return func.__name__
def _DType_reconstruct(scalar_type):
# This is a work-around to pickle type(np.dtype(np.float64)), etc.
# and it should eventually be replaced with a better solution, e.g. when
# DTypes become HeapTypes.
return type(dtype(scalar_type))
def _DType_reduce(DType):
# To pickle a DType without having to add top-level names, pickle the
# scalar type for now (and assume that reconstruction will be possible).
if DType is dtype:
return "dtype" # must pickle `np.dtype` as a singleton.
scalar_type = DType.type # pickle the scalar type for reconstruction
return _DType_reconstruct, (scalar_type,)
import copyreg
copyreg.pickle(ufunc, _ufunc_reduce)
copyreg.pickle(type(dtype), _DType_reduce, _DType_reconstruct)
# Unclutter namespace (must keep _*_reconstruct for unpickling)
del copyreg
del _ufunc_reduce
del _DType_reduce
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester

2
.venv/lib/python3.9/site-packages/numpy/core/__init__.pyi Normal file
View File

@@ -0,0 +1,2 @@
# NOTE: The `np.core` namespace is deliberately kept empty due to it
# being private (despite the lack of leading underscore)

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/__init__.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/_add_newdocs.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/_add_newdocs_scalars.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/_asarray.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/_dtype.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/_dtype_ctypes.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/_exceptions.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/_internal.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/_methods.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/_string_helpers.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/_type_aliases.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/_ufunc_config.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/arrayprint.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/cversions.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/defchararray.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/einsumfunc.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/fromnumeric.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/function_base.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/generate_numpy_api.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/getlimits.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/machar.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/memmap.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/multiarray.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/numeric.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/numerictypes.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/overrides.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/records.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/setup.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/setup_common.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/shape_base.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/umath.cpython-39.pyc Normal file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/__pycache__/umath_tests.cpython-39.pyc Normal file
View File

Binary file not shown.

6530
.venv/lib/python3.9/site-packages/numpy/core/_add_newdocs.py Normal file
View File

File diff suppressed because it is too large Load Diff

259
.venv/lib/python3.9/site-packages/numpy/core/_add_newdocs_scalars.py Normal file
View File

@@ -0,0 +1,259 @@
"""
This file is separate from ``_add_newdocs.py`` so that it can be mocked out by
our sphinx ``conf.py`` during doc builds, where we want to avoid showing
platform-dependent information.
"""
from numpy.core import dtype
from numpy.core import numerictypes as _numerictypes
from numpy.core.function_base import add_newdoc
import platform
##############################################################################
#
# Documentation for concrete scalar classes
#
##############################################################################
def numeric_type_aliases(aliases):
def type_aliases_gen():
for alias, doc in aliases:
try:
alias_type = getattr(_numerictypes, alias)
except AttributeError:
# The set of aliases that actually exist varies between platforms
pass
else:
yield (alias_type, alias, doc)
return list(type_aliases_gen())
possible_aliases = numeric_type_aliases([
('int8', '8-bit signed integer (``-128`` to ``127``)'),
('int16', '16-bit signed integer (``-32_768`` to ``32_767``)'),
('int32', '32-bit signed integer (``-2_147_483_648`` to ``2_147_483_647``)'),
('int64', '64-bit signed integer (``-9_223_372_036_854_775_808`` to ``9_223_372_036_854_775_807``)'),
('intp', 'Signed integer large enough to fit pointer, compatible with C ``intptr_t``'),
('uint8', '8-bit unsigned integer (``0`` to ``255``)'),
('uint16', '16-bit unsigned integer (``0`` to ``65_535``)'),
('uint32', '32-bit unsigned integer (``0`` to ``4_294_967_295``)'),
('uint64', '64-bit unsigned integer (``0`` to ``18_446_744_073_709_551_615``)'),
('uintp', 'Unsigned integer large enough to fit pointer, compatible with C ``uintptr_t``'),
('float16', '16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa'),
('float32', '32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa'),
('float64', '64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa'),
('float96', '96-bit extended-precision floating-point number type'),
('float128', '128-bit extended-precision floating-point number type'),
('complex64', 'Complex number type composed of 2 32-bit-precision floating-point numbers'),
('complex128', 'Complex number type composed of 2 64-bit-precision floating-point numbers'),
('complex192', 'Complex number type composed of 2 96-bit extended-precision floating-point numbers'),
('complex256', 'Complex number type composed of 2 128-bit extended-precision floating-point numbers'),
])
def add_newdoc_for_scalar_type(obj, fixed_aliases, doc):
# note: `:field: value` is rST syntax which renders as field lists.
o = getattr(_numerictypes, obj)
character_code = dtype(o).char
canonical_name_doc = "" if obj == o.__name__ else ":Canonical name: `numpy.{}`\n ".format(obj)
alias_doc = ''.join(":Alias: `numpy.{}`\n ".format(alias) for alias in fixed_aliases)
alias_doc += ''.join(":Alias on this platform ({} {}): `numpy.{}`: {}.\n ".format(platform.system(), platform.machine(), alias, doc)
for (alias_type, alias, doc) in possible_aliases if alias_type is o)
docstring = """
{doc}
:Character code: ``'{character_code}'``
{canonical_name_doc}{alias_doc}
""".format(doc=doc.strip(), character_code=character_code,
canonical_name_doc=canonical_name_doc, alias_doc=alias_doc)
add_newdoc('numpy.core.numerictypes', obj, docstring)
add_newdoc_for_scalar_type('bool_', ['bool8'],
"""
Boolean type (True or False), stored as a byte.
.. warning::
The :class:`bool_` type is not a subclass of the :class:`int_` type
(the :class:`bool_` is not even a number type). This is different
than Python's default implementation of :class:`bool` as a
sub-class of :class:`int`.
""")
add_newdoc_for_scalar_type('byte', [],
"""
Signed integer type, compatible with C ``char``.
""")
add_newdoc_for_scalar_type('short', [],
"""
Signed integer type, compatible with C ``short``.
""")
add_newdoc_for_scalar_type('intc', [],
"""
Signed integer type, compatible with C ``int``.
""")
add_newdoc_for_scalar_type('int_', [],
"""
Signed integer type, compatible with Python `int` and C ``long``.
""")
add_newdoc_for_scalar_type('longlong', [],
"""
Signed integer type, compatible with C ``long long``.
""")
add_newdoc_for_scalar_type('ubyte', [],
"""
Unsigned integer type, compatible with C ``unsigned char``.
""")
add_newdoc_for_scalar_type('ushort', [],
"""
Unsigned integer type, compatible with C ``unsigned short``.
""")
add_newdoc_for_scalar_type('uintc', [],
"""
Unsigned integer type, compatible with C ``unsigned int``.
""")
add_newdoc_for_scalar_type('uint', [],
"""
Unsigned integer type, compatible with C ``unsigned long``.
""")
add_newdoc_for_scalar_type('ulonglong', [],
"""
Signed integer type, compatible with C ``unsigned long long``.
""")
add_newdoc_for_scalar_type('half', [],
"""
Half-precision floating-point number type.
""")
add_newdoc_for_scalar_type('single', [],
"""
Single-precision floating-point number type, compatible with C ``float``.
""")
add_newdoc_for_scalar_type('double', ['float_'],
"""
Double-precision floating-point number type, compatible with Python `float`
and C ``double``.
""")
add_newdoc_for_scalar_type('longdouble', ['longfloat'],
"""
Extended-precision floating-point number type, compatible with C
``long double`` but not necessarily with IEEE 754 quadruple-precision.
""")
add_newdoc_for_scalar_type('csingle', ['singlecomplex'],
"""
Complex number type composed of two single-precision floating-point
numbers.
""")
add_newdoc_for_scalar_type('cdouble', ['cfloat', 'complex_'],
"""
Complex number type composed of two double-precision floating-point
numbers, compatible with Python `complex`.
""")
add_newdoc_for_scalar_type('clongdouble', ['clongfloat', 'longcomplex'],
"""
Complex number type composed of two extended-precision floating-point
numbers.
""")
add_newdoc_for_scalar_type('object_', [],
"""
Any Python object.
""")
add_newdoc_for_scalar_type('str_', ['unicode_'],
r"""
A unicode string.
When used in arrays, this type strips trailing null codepoints.
Unlike the builtin `str`, this supports the :ref:`python:bufferobjects`, exposing its
contents as UCS4:
>>> m = memoryview(np.str_("abc"))
>>> m.format
'3w'
>>> m.tobytes()
b'a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00'
""")
add_newdoc_for_scalar_type('bytes_', ['string_'],
r"""
A byte string.
When used in arrays, this type strips trailing null bytes.
""")
add_newdoc_for_scalar_type('void', [],
r"""
Either an opaque sequence of bytes, or a structure.
>>> np.void(b'abcd')
void(b'\x61\x62\x63\x64')
Structured `void` scalars can only be constructed via extraction from :ref:`structured_arrays`:
>>> arr = np.array((1, 2), dtype=[('x', np.int8), ('y', np.int8)])
>>> arr[()]
(1, 2) # looks like a tuple, but is `np.void`
""")
add_newdoc_for_scalar_type('datetime64', [],
"""
If created from a 64-bit integer, it represents an offset from
``1970-01-01T00:00:00``.
If created from string, the string can be in ISO 8601 date
or datetime format.
>>> np.datetime64(10, 'Y')
numpy.datetime64('1980')
>>> np.datetime64('1980', 'Y')
numpy.datetime64('1980')
>>> np.datetime64(10, 'D')
numpy.datetime64('1970-01-11')
See :ref:`arrays.datetime` for more information.
""")
add_newdoc_for_scalar_type('timedelta64', [],
"""
A timedelta stored as a 64-bit integer.
See :ref:`arrays.datetime` for more information.
""")
# TODO: work out how to put this on the base class, np.floating
for float_name in ('half', 'single', 'double', 'longdouble'):
add_newdoc('numpy.core.numerictypes', float_name, ('as_integer_ratio',
"""
{ftype}.as_integer_ratio() -> (int, int)
Return a pair of integers, whose ratio is exactly equal to the original
floating point number, and with a positive denominator.
Raise `OverflowError` on infinities and a `ValueError` on NaNs.
>>> np.{ftype}(10.0).as_integer_ratio()
(10, 1)
>>> np.{ftype}(0.0).as_integer_ratio()
(0, 1)
>>> np.{ftype}(-.25).as_integer_ratio()
(-1, 4)
""".format(ftype=float_name)))

140
.venv/lib/python3.9/site-packages/numpy/core/_asarray.py Normal file
View File

@@ -0,0 +1,140 @@
"""
Functions in the ``as*array`` family that promote array-likes into arrays.
`require` fits this category despite its name not matching this pattern.
"""
from .overrides import (
array_function_dispatch,
set_array_function_like_doc,
set_module,
)
from .multiarray import array, asanyarray
__all__ = ["require"]
def _require_dispatcher(a, dtype=None, requirements=None, *, like=None):
return (like,)
@set_array_function_like_doc
@set_module('numpy')
def require(a, dtype=None, requirements=None, *, like=None):
"""
Return an ndarray of the provided type that satisfies requirements.
This function is useful to be sure that an array with the correct flags
is returned for passing to compiled code (perhaps through ctypes).
Parameters
----------
a : array_like
The object to be converted to a type-and-requirement-satisfying array.
dtype : data-type
The required data-type. If None preserve the current dtype. If your
application requires the data to be in native byteorder, include
a byteorder specification as a part of the dtype specification.
requirements : str or list of str
The requirements list can be any of the following
* 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array
* 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array
* 'ALIGNED' ('A') - ensure a data-type aligned array
* 'WRITEABLE' ('W') - ensure a writable array
* 'OWNDATA' ('O') - ensure an array that owns its own data
* 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
Array with specified requirements and type if given.
See Also
--------
asarray : Convert input to an ndarray.
asanyarray : Convert to an ndarray, but pass through ndarray subclasses.
ascontiguousarray : Convert input to a contiguous array.
asfortranarray : Convert input to an ndarray with column-major
memory order.
ndarray.flags : Information about the memory layout of the array.
Notes
-----
The returned array will be guaranteed to have the listed requirements
by making a copy if needed.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> x.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
>>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
>>> y.flags
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
"""
if like is not None:
return _require_with_like(
a,
dtype=dtype,
requirements=requirements,
like=like,
)
possible_flags = {'C': 'C', 'C_CONTIGUOUS': 'C', 'CONTIGUOUS': 'C',
'F': 'F', 'F_CONTIGUOUS': 'F', 'FORTRAN': 'F',
'A': 'A', 'ALIGNED': 'A',
'W': 'W', 'WRITEABLE': 'W',
'O': 'O', 'OWNDATA': 'O',
'E': 'E', 'ENSUREARRAY': 'E'}
if not requirements:
return asanyarray(a, dtype=dtype)
else:
requirements = {possible_flags[x.upper()] for x in requirements}
if 'E' in requirements:
requirements.remove('E')
subok = False
else:
subok = True
order = 'A'
if requirements >= {'C', 'F'}:
raise ValueError('Cannot specify both "C" and "F" order')
elif 'F' in requirements:
order = 'F'
requirements.remove('F')
elif 'C' in requirements:
order = 'C'
requirements.remove('C')
arr = array(a, dtype=dtype, order=order, copy=False, subok=subok)
for prop in requirements:
if not arr.flags[prop]:
arr = arr.copy(order)
break
return arr
_require_with_like = array_function_dispatch(
_require_dispatcher
)(require)

81
.venv/lib/python3.9/site-packages/numpy/core/_asarray.pyi Normal file
View File

@@ -0,0 +1,81 @@
import sys
from typing import TypeVar, Union, Iterable, overload
from numpy import ndarray, _OrderKACF
from numpy.typing import ArrayLike, DTypeLike
if sys.version_info >= (3, 8):
from typing import Literal
else:
from typing_extensions import Literal
_ArrayType = TypeVar("_ArrayType", bound=ndarray)
# TODO: The following functions are now defined in C, so should be defined
# in a (not yet existing) `multiarray.pyi`.
# (with the exception of `require`)
def asarray(
a: object,
dtype: DTypeLike = ...,
order: _OrderKACF = ...,
*,
like: ArrayLike = ...
) -> ndarray: ...
@overload
def asanyarray(
a: _ArrayType,
dtype: None = ...,
order: _OrderKACF = ...,
*,
like: ArrayLike = ...
) -> _ArrayType: ...
@overload
def asanyarray(
a: object,
dtype: DTypeLike = ...,
order: _OrderKACF = ...,
*,
like: ArrayLike = ...
) -> ndarray: ...
def ascontiguousarray(
a: object, dtype: DTypeLike = ..., *, like: ArrayLike = ...
) -> ndarray: ...
def asfortranarray(
a: object, dtype: DTypeLike = ..., *, like: ArrayLike = ...
) -> ndarray: ...
_Requirements = Literal[
"C", "C_CONTIGUOUS", "CONTIGUOUS",
"F", "F_CONTIGUOUS", "FORTRAN",
"A", "ALIGNED",
"W", "WRITEABLE",
"O", "OWNDATA"
]
_E = Literal["E", "ENSUREARRAY"]
_RequirementsWithE = Union[_Requirements, _E]
@overload
def require(
a: _ArrayType,
dtype: None = ...,
requirements: Union[None, _Requirements, Iterable[_Requirements]] = ...,
*,
like: ArrayLike = ...
) -> _ArrayType: ...
@overload
def require(
a: object,
dtype: DTypeLike = ...,
requirements: Union[_E, Iterable[_RequirementsWithE]] = ...,
*,
like: ArrayLike = ...
) -> ndarray: ...
@overload
def require(
a: object,
dtype: DTypeLike = ...,
requirements: Union[None, _Requirements, Iterable[_Requirements]] = ...,
*,
like: ArrayLike = ...
) -> ndarray: ...

342
.venv/lib/python3.9/site-packages/numpy/core/_dtype.py Normal file
View File

@@ -0,0 +1,342 @@
"""
A place for code to be called from the implementation of np.dtype
String handling is much easier to do correctly in python.
"""
import numpy as np
_kind_to_stem = {
'u': 'uint',
'i': 'int',
'c': 'complex',
'f': 'float',
'b': 'bool',
'V': 'void',
'O': 'object',
'M': 'datetime',
'm': 'timedelta',
'S': 'bytes',
'U': 'str',
}
def _kind_name(dtype):
try:
return _kind_to_stem[dtype.kind]
except KeyError as e:
raise RuntimeError(
"internal dtype error, unknown kind {!r}"
.format(dtype.kind)
) from None
def __str__(dtype):
if dtype.fields is not None:
return _struct_str(dtype, include_align=True)
elif dtype.subdtype:
return _subarray_str(dtype)
elif issubclass(dtype.type, np.flexible) or not dtype.isnative:
return dtype.str
else:
return dtype.name
def __repr__(dtype):
arg_str = _construction_repr(dtype, include_align=False)
if dtype.isalignedstruct:
arg_str = arg_str + ", align=True"
return "dtype({})".format(arg_str)
def _unpack_field(dtype, offset, title=None):
"""
Helper function to normalize the items in dtype.fields.
Call as:
dtype, offset, title = _unpack_field(*dtype.fields[name])
"""
return dtype, offset, title
def _isunsized(dtype):
# PyDataType_ISUNSIZED
return dtype.itemsize == 0
def _construction_repr(dtype, include_align=False, short=False):
"""
Creates a string repr of the dtype, excluding the 'dtype()' part
surrounding the object. This object may be a string, a list, or
a dict depending on the nature of the dtype. This
is the object passed as the first parameter to the dtype
constructor, and if no additional constructor parameters are
given, will reproduce the exact memory layout.
Parameters
----------
short : bool
If true, this creates a shorter repr using 'kind' and 'itemsize', instead
of the longer type name.
include_align : bool
If true, this includes the 'align=True' parameter
inside the struct dtype construction dict when needed. Use this flag
if you want a proper repr string without the 'dtype()' part around it.
If false, this does not preserve the
'align=True' parameter or sticky NPY_ALIGNED_STRUCT flag for
struct arrays like the regular repr does, because the 'align'
flag is not part of first dtype constructor parameter. This
mode is intended for a full 'repr', where the 'align=True' is
provided as the second parameter.
"""
if dtype.fields is not None:
return _struct_str(dtype, include_align=include_align)
elif dtype.subdtype:
return _subarray_str(dtype)
else:
return _scalar_str(dtype, short=short)
def _scalar_str(dtype, short):
byteorder = _byte_order_str(dtype)
if dtype.type == np.bool_:
if short:
return "'?'"
else:
return "'bool'"
elif dtype.type == np.object_:
# The object reference may be different sizes on different
# platforms, so it should never include the itemsize here.
return "'O'"
elif dtype.type == np.string_:
if _isunsized(dtype):
return "'S'"
else:
return "'S%d'" % dtype.itemsize
elif dtype.type == np.unicode_:
if _isunsized(dtype):
return "'%sU'" % byteorder
else:
return "'%sU%d'" % (byteorder, dtype.itemsize / 4)
# unlike the other types, subclasses of void are preserved - but
# historically the repr does not actually reveal the subclass
elif issubclass(dtype.type, np.void):
if _isunsized(dtype):
return "'V'"
else:
return "'V%d'" % dtype.itemsize
elif dtype.type == np.datetime64:
return "'%sM8%s'" % (byteorder, _datetime_metadata_str(dtype))
elif dtype.type == np.timedelta64:
return "'%sm8%s'" % (byteorder, _datetime_metadata_str(dtype))
elif np.issubdtype(dtype, np.number):
# Short repr with endianness, like '<f8'
if short or dtype.byteorder not in ('=', '|'):
return "'%s%c%d'" % (byteorder, dtype.kind, dtype.itemsize)
# Longer repr, like 'float64'
else:
return "'%s%d'" % (_kind_name(dtype), 8*dtype.itemsize)
elif dtype.isbuiltin == 2:
return dtype.type.__name__
else:
raise RuntimeError(
"Internal error: NumPy dtype unrecognized type number")
def _byte_order_str(dtype):
""" Normalize byteorder to '<' or '>' """
# hack to obtain the native and swapped byte order characters
swapped = np.dtype(int).newbyteorder('S')
native = swapped.newbyteorder('S')
byteorder = dtype.byteorder
if byteorder == '=':
return native.byteorder
if byteorder == 'S':
# TODO: this path can never be reached
return swapped.byteorder
elif byteorder == '|':
return ''
else:
return byteorder
def _datetime_metadata_str(dtype):
# TODO: this duplicates the C metastr_to_unicode functionality
unit, count = np.datetime_data(dtype)
if unit == 'generic':
return ''
elif count == 1:
return '[{}]'.format(unit)
else:
return '[{}{}]'.format(count, unit)
def _struct_dict_str(dtype, includealignedflag):
# unpack the fields dictionary into ls
names = dtype.names
fld_dtypes = []
offsets = []
titles = []
for name in names:
fld_dtype, offset, title = _unpack_field(*dtype.fields[name])
fld_dtypes.append(fld_dtype)
offsets.append(offset)
titles.append(title)
# Build up a string to make the dictionary
# First, the names
ret = "{'names':["
ret += ",".join(repr(name) for name in names)
# Second, the formats
ret += "], 'formats':["
ret += ",".join(
_construction_repr(fld_dtype, short=True) for fld_dtype in fld_dtypes)
# Third, the offsets
ret += "], 'offsets':["
ret += ",".join("%d" % offset for offset in offsets)
# Fourth, the titles
if any(title is not None for title in titles):
ret += "], 'titles':["
ret += ",".join(repr(title) for title in titles)
# Fifth, the itemsize
ret += "], 'itemsize':%d" % dtype.itemsize
if (includealignedflag and dtype.isalignedstruct):
# Finally, the aligned flag
ret += ", 'aligned':True}"
else:
ret += "}"
return ret
def _is_packed(dtype):
"""
Checks whether the structured data type in 'dtype'
has a simple layout, where all the fields are in order,
and follow each other with no alignment padding.
When this returns true, the dtype can be reconstructed
from a list of the field names and dtypes with no additional
dtype parameters.
Duplicates the C `is_dtype_struct_simple_unaligned_layout` function.
"""
total_offset = 0
for name in dtype.names:
fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
if fld_offset != total_offset:
return False
total_offset += fld_dtype.itemsize
if total_offset != dtype.itemsize:
return False
return True
def _struct_list_str(dtype):
items = []
for name in dtype.names:
fld_dtype, fld_offset, title = _unpack_field(*dtype.fields[name])
item = "("
if title is not None:
item += "({!r}, {!r}), ".format(title, name)
else:
item += "{!r}, ".format(name)
# Special case subarray handling here
if fld_dtype.subdtype is not None:
base, shape = fld_dtype.subdtype
item += "{}, {}".format(
_construction_repr(base, short=True),
shape
)
else:
item += _construction_repr(fld_dtype, short=True)
item += ")"
items.append(item)
return "[" + ", ".join(items) + "]"
def _struct_str(dtype, include_align):
# The list str representation can't include the 'align=' flag,
# so if it is requested and the struct has the aligned flag set,
# we must use the dict str instead.
if not (include_align and dtype.isalignedstruct) and _is_packed(dtype):
sub = _struct_list_str(dtype)
else:
sub = _struct_dict_str(dtype, include_align)
# If the data type isn't the default, void, show it
if dtype.type != np.void:
return "({t.__module__}.{t.__name__}, {f})".format(t=dtype.type, f=sub)
else:
return sub
def _subarray_str(dtype):
base, shape = dtype.subdtype
return "({}, {})".format(
_construction_repr(base, short=True),
shape
)
def _name_includes_bit_suffix(dtype):
if dtype.type == np.object_:
# pointer size varies by system, best to omit it
return False
elif dtype.type == np.bool_:
# implied
return False
elif np.issubdtype(dtype, np.flexible) and _isunsized(dtype):
# unspecified
return False
else:
return True
def _name_get(dtype):
# provides dtype.name.__get__, documented as returning a "bit name"
if dtype.isbuiltin == 2:
# user dtypes don't promise to do anything special
return dtype.type.__name__
if issubclass(dtype.type, np.void):
# historically, void subclasses preserve their name, eg `record64`
name = dtype.type.__name__
else:
name = _kind_name(dtype)
# append bit counts
if _name_includes_bit_suffix(dtype):
name += "{}".format(dtype.itemsize * 8)
# append metadata to datetimes
if dtype.type in (np.datetime64, np.timedelta64):
name += _datetime_metadata_str(dtype)
return name

117
.venv/lib/python3.9/site-packages/numpy/core/_dtype_ctypes.py Normal file
View File

@@ -0,0 +1,117 @@
"""
Conversion from ctypes to dtype.
In an ideal world, we could achieve this through the PEP3118 buffer protocol,
something like::
def dtype_from_ctypes_type(t):
# needed to ensure that the shape of `t` is within memoryview.format
class DummyStruct(ctypes.Structure):
_fields_ = [('a', t)]
# empty to avoid memory allocation
ctype_0 = (DummyStruct * 0)()
mv = memoryview(ctype_0)
# convert the struct, and slice back out the field
return _dtype_from_pep3118(mv.format)['a']
Unfortunately, this fails because:
* ctypes cannot handle length-0 arrays with PEP3118 (bpo-32782)
* PEP3118 cannot represent unions, but both numpy and ctypes can
* ctypes cannot handle big-endian structs with PEP3118 (bpo-32780)
"""
# We delay-import ctypes for distributions that do not include it.
# While this module is not used unless the user passes in ctypes
# members, it is eagerly imported from numpy/core/__init__.py.
import numpy as np
def _from_ctypes_array(t):
return np.dtype((dtype_from_ctypes_type(t._type_), (t._length_,)))
def _from_ctypes_structure(t):
for item in t._fields_:
if len(item) > 2:
raise TypeError(
"ctypes bitfields have no dtype equivalent")
if hasattr(t, "_pack_"):
import ctypes
formats = []
offsets = []
names = []
current_offset = 0
for fname, ftyp in t._fields_:
names.append(fname)
formats.append(dtype_from_ctypes_type(ftyp))
# Each type has a default offset, this is platform dependent for some types.
effective_pack = min(t._pack_, ctypes.alignment(ftyp))
current_offset = ((current_offset + effective_pack - 1) // effective_pack) * effective_pack
offsets.append(current_offset)
current_offset += ctypes.sizeof(ftyp)
return np.dtype(dict(
formats=formats,
offsets=offsets,
names=names,
itemsize=ctypes.sizeof(t)))
else:
fields = []
for fname, ftyp in t._fields_:
fields.append((fname, dtype_from_ctypes_type(ftyp)))
# by default, ctypes structs are aligned
return np.dtype(fields, align=True)
def _from_ctypes_scalar(t):
"""
Return the dtype type with endianness included if it's the case
"""
if getattr(t, '__ctype_be__', None) is t:
return np.dtype('>' + t._type_)
elif getattr(t, '__ctype_le__', None) is t:
return np.dtype('<' + t._type_)
else:
return np.dtype(t._type_)
def _from_ctypes_union(t):
import ctypes
formats = []
offsets = []
names = []
for fname, ftyp in t._fields_:
names.append(fname)
formats.append(dtype_from_ctypes_type(ftyp))
offsets.append(0) # Union fields are offset to 0
return np.dtype(dict(
formats=formats,
offsets=offsets,
names=names,
itemsize=ctypes.sizeof(t)))
def dtype_from_ctypes_type(t):
"""
Construct a dtype object from a ctypes type
"""
import _ctypes
if issubclass(t, _ctypes.Array):
return _from_ctypes_array(t)
elif issubclass(t, _ctypes._Pointer):
raise TypeError("ctypes pointers have no dtype equivalent")
elif issubclass(t, _ctypes.Structure):
return _from_ctypes_structure(t)
elif issubclass(t, _ctypes.Union):
return _from_ctypes_union(t)
elif isinstance(getattr(t, '_type_', None), str):
return _from_ctypes_scalar(t)
else:
raise NotImplementedError(
"Unknown ctypes type {}".format(t.__name__))

197
.venv/lib/python3.9/site-packages/numpy/core/_exceptions.py Normal file
View File

@@ -0,0 +1,197 @@
"""
Various richly-typed exceptions, that also help us deal with string formatting
in python where it's easier.
By putting the formatting in `__str__`, we also avoid paying the cost for
users who silence the exceptions.
"""
from numpy.core.overrides import set_module
def _unpack_tuple(tup):
if len(tup) == 1:
return tup[0]
else:
return tup
def _display_as_base(cls):
"""
A decorator that makes an exception class look like its base.
We use this to hide subclasses that are implementation details - the user
should catch the base type, which is what the traceback will show them.
Classes decorated with this decorator are subject to removal without a
deprecation warning.
"""
assert issubclass(cls, Exception)
cls.__name__ = cls.__base__.__name__
return cls
class UFuncTypeError(TypeError):
""" Base class for all ufunc exceptions """
def __init__(self, ufunc):
self.ufunc = ufunc
@_display_as_base
class _UFuncBinaryResolutionError(UFuncTypeError):
""" Thrown when a binary resolution fails """
def __init__(self, ufunc, dtypes):
super().__init__(ufunc)
self.dtypes = tuple(dtypes)
assert len(self.dtypes) == 2
def __str__(self):
return (
"ufunc {!r} cannot use operands with types {!r} and {!r}"
).format(
self.ufunc.__name__, *self.dtypes
)
@_display_as_base
class _UFuncNoLoopError(UFuncTypeError):
""" Thrown when a ufunc loop cannot be found """
def __init__(self, ufunc, dtypes):
super().__init__(ufunc)
self.dtypes = tuple(dtypes)
def __str__(self):
return (
"ufunc {!r} did not contain a loop with signature matching types "
"{!r} -> {!r}"
).format(
self.ufunc.__name__,
_unpack_tuple(self.dtypes[:self.ufunc.nin]),
_unpack_tuple(self.dtypes[self.ufunc.nin:])
)
@_display_as_base
class _UFuncCastingError(UFuncTypeError):
def __init__(self, ufunc, casting, from_, to):
super().__init__(ufunc)
self.casting = casting
self.from_ = from_
self.to = to
@_display_as_base
class _UFuncInputCastingError(_UFuncCastingError):
""" Thrown when a ufunc input cannot be casted """
def __init__(self, ufunc, casting, from_, to, i):
super().__init__(ufunc, casting, from_, to)
self.in_i = i
def __str__(self):
# only show the number if more than one input exists
i_str = "{} ".format(self.in_i) if self.ufunc.nin != 1 else ""
return (
"Cannot cast ufunc {!r} input {}from {!r} to {!r} with casting "
"rule {!r}"
).format(
self.ufunc.__name__, i_str, self.from_, self.to, self.casting
)
@_display_as_base
class _UFuncOutputCastingError(_UFuncCastingError):
""" Thrown when a ufunc output cannot be casted """
def __init__(self, ufunc, casting, from_, to, i):
super().__init__(ufunc, casting, from_, to)
self.out_i = i
def __str__(self):
# only show the number if more than one output exists
i_str = "{} ".format(self.out_i) if self.ufunc.nout != 1 else ""
return (
"Cannot cast ufunc {!r} output {}from {!r} to {!r} with casting "
"rule {!r}"
).format(
self.ufunc.__name__, i_str, self.from_, self.to, self.casting
)
# Exception used in shares_memory()
@set_module('numpy')
class TooHardError(RuntimeError):
pass
@set_module('numpy')
class AxisError(ValueError, IndexError):
""" Axis supplied was invalid. """
def __init__(self, axis, ndim=None, msg_prefix=None):
# single-argument form just delegates to base class
if ndim is None and msg_prefix is None:
msg = axis
# do the string formatting here, to save work in the C code
else:
msg = ("axis {} is out of bounds for array of dimension {}"
.format(axis, ndim))
if msg_prefix is not None:
msg = "{}: {}".format(msg_prefix, msg)
super().__init__(msg)
@_display_as_base
class _ArrayMemoryError(MemoryError):
""" Thrown when an array cannot be allocated"""
def __init__(self, shape, dtype):
self.shape = shape
self.dtype = dtype
@property
def _total_size(self):
num_bytes = self.dtype.itemsize
for dim in self.shape:
num_bytes *= dim
return num_bytes
@staticmethod
def _size_to_string(num_bytes):
""" Convert a number of bytes into a binary size string """
# https://en.wikipedia.org/wiki/Binary_prefix
LOG2_STEP = 10
STEP = 1024
units = ['bytes', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB']
unit_i = max(num_bytes.bit_length() - 1, 1) // LOG2_STEP
unit_val = 1 << (unit_i * LOG2_STEP)
n_units = num_bytes / unit_val
del unit_val
# ensure we pick a unit that is correct after rounding
if round(n_units) == STEP:
unit_i += 1
n_units /= STEP
# deal with sizes so large that we don't have units for them
if unit_i >= len(units):
new_unit_i = len(units) - 1
n_units *= 1 << ((unit_i - new_unit_i) * LOG2_STEP)
unit_i = new_unit_i
unit_name = units[unit_i]
# format with a sensible number of digits
if unit_i == 0:
# no decimal point on bytes
return '{:.0f} {}'.format(n_units, unit_name)
elif round(n_units) < 1000:
# 3 significant figures, if none are dropped to the left of the .
return '{:#.3g} {}'.format(n_units, unit_name)
else:
# just give all the digits otherwise
return '{:#.0f} {}'.format(n_units, unit_name)
def __str__(self):
size_str = self._size_to_string(self._total_size)
return (
"Unable to allocate {} for an array with shape {} and data type {}"
.format(size_str, self.shape, self.dtype)
)

910
.venv/lib/python3.9/site-packages/numpy/core/_internal.py Normal file
View File

@@ -0,0 +1,910 @@
"""
A place for internal code
Some things are more easily handled Python.
"""
import ast
import re
import sys
import platform
import warnings
from .multiarray import dtype, array, ndarray
try:
import ctypes
except ImportError:
ctypes = None
IS_PYPY = platform.python_implementation() == 'PyPy'
if sys.byteorder == 'little':
_nbo = '<'
else:
_nbo = '>'
def _makenames_list(adict, align):
allfields = []
for fname, obj in adict.items():
n = len(obj)
if not isinstance(obj, tuple) or n not in (2, 3):
raise ValueError("entry not a 2- or 3- tuple")
if n > 2 and obj[2] == fname:
continue
num = int(obj[1])
if num < 0:
raise ValueError("invalid offset.")
format = dtype(obj[0], align=align)
if n > 2:
title = obj[2]
else:
title = None
allfields.append((fname, format, num, title))
# sort by offsets
allfields.sort(key=lambda x: x[2])
names = [x[0] for x in allfields]
formats = [x[1] for x in allfields]
offsets = [x[2] for x in allfields]
titles = [x[3] for x in allfields]
return names, formats, offsets, titles
# Called in PyArray_DescrConverter function when
# a dictionary without "names" and "formats"
# fields is used as a data-type descriptor.
def _usefields(adict, align):
try:
names = adict[-1]
except KeyError:
names = None
if names is None:
names, formats, offsets, titles = _makenames_list(adict, align)
else:
formats = []
offsets = []
titles = []
for name in names:
res = adict[name]
formats.append(res[0])
offsets.append(res[1])
if len(res) > 2:
titles.append(res[2])
else:
titles.append(None)
return dtype({"names": names,
"formats": formats,
"offsets": offsets,
"titles": titles}, align)
# construct an array_protocol descriptor list
# from the fields attribute of a descriptor
# This calls itself recursively but should eventually hit
# a descriptor that has no fields and then return
# a simple typestring
def _array_descr(descriptor):
fields = descriptor.fields
if fields is None:
subdtype = descriptor.subdtype
if subdtype is None:
if descriptor.metadata is None:
return descriptor.str
else:
new = descriptor.metadata.copy()
if new:
return (descriptor.str, new)
else:
return descriptor.str
else:
return (_array_descr(subdtype[0]), subdtype[1])
names = descriptor.names
ordered_fields = [fields[x] + (x,) for x in names]
result = []
offset = 0
for field in ordered_fields:
if field[1] > offset:
num = field[1] - offset
result.append(('', f'|V{num}'))
offset += num
elif field[1] < offset:
raise ValueError(
"dtype.descr is not defined for types with overlapping or "
"out-of-order fields")
if len(field) > 3:
name = (field[2], field[3])
else:
name = field[2]
if field[0].subdtype:
tup = (name, _array_descr(field[0].subdtype[0]),
field[0].subdtype[1])
else:
tup = (name, _array_descr(field[0]))
offset += field[0].itemsize
result.append(tup)
if descriptor.itemsize > offset:
num = descriptor.itemsize - offset
result.append(('', f'|V{num}'))
return result
# Build a new array from the information in a pickle.
# Note that the name numpy.core._internal._reconstruct is embedded in
# pickles of ndarrays made with NumPy before release 1.0
# so don't remove the name here, or you'll
# break backward compatibility.
def _reconstruct(subtype, shape, dtype):
return ndarray.__new__(subtype, shape, dtype)
# format_re was originally from numarray by J. Todd Miller
format_re = re.compile(r'(?P<order1>[<>|=]?)'
r'(?P<repeats> *[(]?[ ,0-9]*[)]? *)'
r'(?P<order2>[<>|=]?)'
r'(?P<dtype>[A-Za-z0-9.?]*(?:\[[a-zA-Z0-9,.]+\])?)')
sep_re = re.compile(r'\s*,\s*')
space_re = re.compile(r'\s+$')
# astr is a string (perhaps comma separated)
_convorder = {'=': _nbo}
def _commastring(astr):
startindex = 0
result = []
while startindex < len(astr):
mo = format_re.match(astr, pos=startindex)
try:
(order1, repeats, order2, dtype) = mo.groups()
except (TypeError, AttributeError):
raise ValueError(
f'format number {len(result)+1} of "{astr}" is not recognized'
) from None
startindex = mo.end()
# Separator or ending padding
if startindex < len(astr):
if space_re.match(astr, pos=startindex):
startindex = len(astr)
else:
mo = sep_re.match(astr, pos=startindex)
if not mo:
raise ValueError(
'format number %d of "%s" is not recognized' %
(len(result)+1, astr))
startindex = mo.end()
if order2 == '':
order = order1
elif order1 == '':
order = order2
else:
order1 = _convorder.get(order1, order1)
order2 = _convorder.get(order2, order2)
if (order1 != order2):
raise ValueError(
'inconsistent byte-order specification %s and %s' %
(order1, order2))
order = order1
if order in ('|', '=', _nbo):
order = ''
dtype = order + dtype
if (repeats == ''):
newitem = dtype
else:
newitem = (dtype, ast.literal_eval(repeats))
result.append(newitem)
return result
class dummy_ctype:
def __init__(self, cls):
self._cls = cls
def __mul__(self, other):
return self
def __call__(self, *other):
return self._cls(other)
def __eq__(self, other):
return self._cls == other._cls
def __ne__(self, other):
return self._cls != other._cls
def _getintp_ctype():
val = _getintp_ctype.cache
if val is not None:
return val
if ctypes is None:
import numpy as np
val = dummy_ctype(np.intp)
else:
char = dtype('p').char
if char == 'i':
val = ctypes.c_int
elif char == 'l':
val = ctypes.c_long
elif char == 'q':
val = ctypes.c_longlong
else:
val = ctypes.c_long
_getintp_ctype.cache = val
return val
_getintp_ctype.cache = None
# Used for .ctypes attribute of ndarray
class _missing_ctypes:
def cast(self, num, obj):
return num.value
class c_void_p:
def __init__(self, ptr):
self.value = ptr
class _ctypes:
def __init__(self, array, ptr=None):
self._arr = array
if ctypes:
self._ctypes = ctypes
self._data = self._ctypes.c_void_p(ptr)
else:
# fake a pointer-like object that holds onto the reference
self._ctypes = _missing_ctypes()
self._data = self._ctypes.c_void_p(ptr)
self._data._objects = array
if self._arr.ndim == 0:
self._zerod = True
else:
self._zerod = False
def data_as(self, obj):
"""
Return the data pointer cast to a particular c-types object.
For example, calling ``self._as_parameter_`` is equivalent to
``self.data_as(ctypes.c_void_p)``. Perhaps you want to use the data as a
pointer to a ctypes array of floating-point data:
``self.data_as(ctypes.POINTER(ctypes.c_double))``.
The returned pointer will keep a reference to the array.
"""
# _ctypes.cast function causes a circular reference of self._data in
# self._data._objects. Attributes of self._data cannot be released
# until gc.collect is called. Make a copy of the pointer first then let
# it hold the array reference. This is a workaround to circumvent the
# CPython bug https://bugs.python.org/issue12836
ptr = self._ctypes.cast(self._data, obj)
ptr._arr = self._arr
return ptr
def shape_as(self, obj):
"""
Return the shape tuple as an array of some other c-types
type. For example: ``self.shape_as(ctypes.c_short)``.
"""
if self._zerod:
return None
return (obj*self._arr.ndim)(*self._arr.shape)
def strides_as(self, obj):
"""
Return the strides tuple as an array of some other
c-types type. For example: ``self.strides_as(ctypes.c_longlong)``.
"""
if self._zerod:
return None
return (obj*self._arr.ndim)(*self._arr.strides)
@property
def data(self):
"""
A pointer to the memory area of the array as a Python integer.
This memory area may contain data that is not aligned, or not in correct
byte-order. The memory area may not even be writeable. The array
flags and data-type of this array should be respected when passing this
attribute to arbitrary C-code to avoid trouble that can include Python
crashing. User Beware! The value of this attribute is exactly the same
as ``self._array_interface_['data'][0]``.
Note that unlike ``data_as``, a reference will not be kept to the array:
code like ``ctypes.c_void_p((a + b).ctypes.data)`` will result in a
pointer to a deallocated array, and should be spelt
``(a + b).ctypes.data_as(ctypes.c_void_p)``
"""
return self._data.value
@property
def shape(self):
"""
(c_intp*self.ndim): A ctypes array of length self.ndim where
the basetype is the C-integer corresponding to ``dtype('p')`` on this
platform. This base-type could be `ctypes.c_int`, `ctypes.c_long`, or
`ctypes.c_longlong` depending on the platform.
The c_intp type is defined accordingly in `numpy.ctypeslib`.
The ctypes array contains the shape of the underlying array.
"""
return self.shape_as(_getintp_ctype())
@property
def strides(self):
"""
(c_intp*self.ndim): A ctypes array of length self.ndim where
the basetype is the same as for the shape attribute. This ctypes array
contains the strides information from the underlying array. This strides
information is important for showing how many bytes must be jumped to
get to the next element in the array.
"""
return self.strides_as(_getintp_ctype())
@property
def _as_parameter_(self):
"""
Overrides the ctypes semi-magic method
Enables `c_func(some_array.ctypes)`
"""
return self.data_as(ctypes.c_void_p)
# Numpy 1.21.0, 2021-05-18
def get_data(self):
"""Deprecated getter for the `_ctypes.data` property.
.. deprecated:: 1.21
"""
warnings.warn('"get_data" is deprecated. Use "data" instead',
DeprecationWarning, stacklevel=2)
return self.data
def get_shape(self):
"""Deprecated getter for the `_ctypes.shape` property.
.. deprecated:: 1.21
"""
warnings.warn('"get_shape" is deprecated. Use "shape" instead',
DeprecationWarning, stacklevel=2)
return self.shape
def get_strides(self):
"""Deprecated getter for the `_ctypes.strides` property.
.. deprecated:: 1.21
"""
warnings.warn('"get_strides" is deprecated. Use "strides" instead',
DeprecationWarning, stacklevel=2)
return self.strides
def get_as_parameter(self):
"""Deprecated getter for the `_ctypes._as_parameter_` property.
.. deprecated:: 1.21
"""
warnings.warn(
'"get_as_parameter" is deprecated. Use "_as_parameter_" instead',
DeprecationWarning, stacklevel=2,
)
return self._as_parameter_
def _newnames(datatype, order):
"""
Given a datatype and an order object, return a new names tuple, with the
order indicated
"""
oldnames = datatype.names
nameslist = list(oldnames)
if isinstance(order, str):
order = [order]
seen = set()
if isinstance(order, (list, tuple)):
for name in order:
try:
nameslist.remove(name)
except ValueError:
if name in seen:
raise ValueError(f"duplicate field name: {name}") from None
else:
raise ValueError(f"unknown field name: {name}") from None
seen.add(name)
return tuple(list(order) + nameslist)
raise ValueError(f"unsupported order value: {order}")
def _copy_fields(ary):
"""Return copy of structured array with padding between fields removed.
Parameters
----------
ary : ndarray
Structured array from which to remove padding bytes
Returns
-------
ary_copy : ndarray
Copy of ary with padding bytes removed
"""
dt = ary.dtype
copy_dtype = {'names': dt.names,
'formats': [dt.fields[name][0] for name in dt.names]}
return array(ary, dtype=copy_dtype, copy=True)
def _getfield_is_safe(oldtype, newtype, offset):
""" Checks safety of getfield for object arrays.
As in _view_is_safe, we need to check that memory containing objects is not
reinterpreted as a non-object datatype and vice versa.
Parameters
----------
oldtype : data-type
Data type of the original ndarray.
newtype : data-type
Data type of the field being accessed by ndarray.getfield
offset : int
Offset of the field being accessed by ndarray.getfield
Raises
------
TypeError
If the field access is invalid
"""
if newtype.hasobject or oldtype.hasobject:
if offset == 0 and newtype == oldtype:
return
if oldtype.names is not None:
for name in oldtype.names:
if (oldtype.fields[name][1] == offset and
oldtype.fields[name][0] == newtype):
return
raise TypeError("Cannot get/set field of an object array")
return
def _view_is_safe(oldtype, newtype):
""" Checks safety of a view involving object arrays, for example when
doing::
np.zeros(10, dtype=oldtype).view(newtype)
Parameters
----------
oldtype : data-type
Data type of original ndarray
newtype : data-type
Data type of the view
Raises
------
TypeError
If the new type is incompatible with the old type.
"""
# if the types are equivalent, there is no problem.
# for example: dtype((np.record, 'i4,i4')) == dtype((np.void, 'i4,i4'))
if oldtype == newtype:
return
if newtype.hasobject or oldtype.hasobject:
raise TypeError("Cannot change data-type for object array.")
return
# Given a string containing a PEP 3118 format specifier,
# construct a NumPy dtype
_pep3118_native_map = {
'?': '?',
'c': 'S1',
'b': 'b',
'B': 'B',
'h': 'h',
'H': 'H',
'i': 'i',
'I': 'I',
'l': 'l',
'L': 'L',
'q': 'q',
'Q': 'Q',
'e': 'e',
'f': 'f',
'd': 'd',
'g': 'g',
'Zf': 'F',
'Zd': 'D',
'Zg': 'G',
's': 'S',
'w': 'U',
'O': 'O',
'x': 'V', # padding
}
_pep3118_native_typechars = ''.join(_pep3118_native_map.keys())
_pep3118_standard_map = {
'?': '?',
'c': 'S1',
'b': 'b',
'B': 'B',
'h': 'i2',
'H': 'u2',
'i': 'i4',
'I': 'u4',
'l': 'i4',
'L': 'u4',
'q': 'i8',
'Q': 'u8',
'e': 'f2',
'f': 'f',
'd': 'd',
'Zf': 'F',
'Zd': 'D',
's': 'S',
'w': 'U',
'O': 'O',
'x': 'V', # padding
}
_pep3118_standard_typechars = ''.join(_pep3118_standard_map.keys())
_pep3118_unsupported_map = {
'u': 'UCS-2 strings',
'&': 'pointers',
't': 'bitfields',
'X': 'function pointers',
}
class _Stream:
def __init__(self, s):
self.s = s
self.byteorder = '@'
def advance(self, n):
res = self.s[:n]
self.s = self.s[n:]
return res
def consume(self, c):
if self.s[:len(c)] == c:
self.advance(len(c))
return True
return False
def consume_until(self, c):
if callable(c):
i = 0
while i < len(self.s) and not c(self.s[i]):
i = i + 1
return self.advance(i)
else:
i = self.s.index(c)
res = self.advance(i)
self.advance(len(c))
return res
@property
def next(self):
return self.s[0]
def __bool__(self):
return bool(self.s)
def _dtype_from_pep3118(spec):
stream = _Stream(spec)
dtype, align = __dtype_from_pep3118(stream, is_subdtype=False)
return dtype
def __dtype_from_pep3118(stream, is_subdtype):
field_spec = dict(
names=[],
formats=[],
offsets=[],
itemsize=0
)
offset = 0
common_alignment = 1
is_padding = False
# Parse spec
while stream:
value = None
# End of structure, bail out to upper level
if stream.consume('}'):
break
# Sub-arrays (1)
shape = None
if stream.consume('('):
shape = stream.consume_until(')')
shape = tuple(map(int, shape.split(',')))
# Byte order
if stream.next in ('@', '=', '<', '>', '^', '!'):
byteorder = stream.advance(1)
if byteorder == '!':
byteorder = '>'
stream.byteorder = byteorder
# Byte order characters also control native vs. standard type sizes
if stream.byteorder in ('@', '^'):
type_map = _pep3118_native_map
type_map_chars = _pep3118_native_typechars
else:
type_map = _pep3118_standard_map
type_map_chars = _pep3118_standard_typechars
# Item sizes
itemsize_str = stream.consume_until(lambda c: not c.isdigit())
if itemsize_str:
itemsize = int(itemsize_str)
else:
itemsize = 1
# Data types
is_padding = False
if stream.consume('T{'):
value, align = __dtype_from_pep3118(
stream, is_subdtype=True)
elif stream.next in type_map_chars:
if stream.next == 'Z':
typechar = stream.advance(2)
else:
typechar = stream.advance(1)
is_padding = (typechar == 'x')
dtypechar = type_map[typechar]
if dtypechar in 'USV':
dtypechar += '%d' % itemsize
itemsize = 1
numpy_byteorder = {'@': '=', '^': '='}.get(
stream.byteorder, stream.byteorder)
value = dtype(numpy_byteorder + dtypechar)
align = value.alignment
elif stream.next in _pep3118_unsupported_map:
desc = _pep3118_unsupported_map[stream.next]
raise NotImplementedError(
"Unrepresentable PEP 3118 data type {!r} ({})"
.format(stream.next, desc))
else:
raise ValueError("Unknown PEP 3118 data type specifier %r" % stream.s)
#
# Native alignment may require padding
#
# Here we assume that the presence of a '@' character implicitly implies
# that the start of the array is *already* aligned.
#
extra_offset = 0
if stream.byteorder == '@':
start_padding = (-offset) % align
intra_padding = (-value.itemsize) % align
offset += start_padding
if intra_padding != 0:
if itemsize > 1 or (shape is not None and _prod(shape) > 1):
# Inject internal padding to the end of the sub-item
value = _add_trailing_padding(value, intra_padding)
else:
# We can postpone the injection of internal padding,
# as the item appears at most once
extra_offset += intra_padding
# Update common alignment
common_alignment = _lcm(align, common_alignment)
# Convert itemsize to sub-array
if itemsize != 1:
value = dtype((value, (itemsize,)))
# Sub-arrays (2)
if shape is not None:
value = dtype((value, shape))
# Field name
if stream.consume(':'):
name = stream.consume_until(':')
else:
name = None
if not (is_padding and name is None):
if name is not None and name in field_spec['names']:
raise RuntimeError(f"Duplicate field name '{name}' in PEP3118 format")
field_spec['names'].append(name)
field_spec['formats'].append(value)
field_spec['offsets'].append(offset)
offset += value.itemsize
offset += extra_offset
field_spec['itemsize'] = offset
# extra final padding for aligned types
if stream.byteorder == '@':
field_spec['itemsize'] += (-offset) % common_alignment
# Check if this was a simple 1-item type, and unwrap it
if (field_spec['names'] == [None]
and field_spec['offsets'][0] == 0
and field_spec['itemsize'] == field_spec['formats'][0].itemsize
and not is_subdtype):
ret = field_spec['formats'][0]
else:
_fix_names(field_spec)
ret = dtype(field_spec)
# Finished
return ret, common_alignment
def _fix_names(field_spec):
""" Replace names which are None with the next unused f%d name """
names = field_spec['names']
for i, name in enumerate(names):
if name is not None:
continue
j = 0
while True:
name = f'f{j}'
if name not in names:
break
j = j + 1
names[i] = name
def _add_trailing_padding(value, padding):
"""Inject the specified number of padding bytes at the end of a dtype"""
if value.fields is None:
field_spec = dict(
names=['f0'],
formats=[value],
offsets=[0],
itemsize=value.itemsize
)
else:
fields = value.fields
names = value.names
field_spec = dict(
names=names,
formats=[fields[name][0] for name in names],
offsets=[fields[name][1] for name in names],
itemsize=value.itemsize
)
field_spec['itemsize'] += padding
return dtype(field_spec)
def _prod(a):
p = 1
for x in a:
p *= x
return p
def _gcd(a, b):
"""Calculate the greatest common divisor of a and b"""
while b:
a, b = b, a % b
return a
def _lcm(a, b):
return a // _gcd(a, b) * b
def array_ufunc_errmsg_formatter(dummy, ufunc, method, *inputs, **kwargs):
""" Format the error message for when __array_ufunc__ gives up. """
args_string = ', '.join(['{!r}'.format(arg) for arg in inputs] +
['{}={!r}'.format(k, v)
for k, v in kwargs.items()])
args = inputs + kwargs.get('out', ())
types_string = ', '.join(repr(type(arg).__name__) for arg in args)
return ('operand type(s) all returned NotImplemented from '
'__array_ufunc__({!r}, {!r}, {}): {}'
.format(ufunc, method, args_string, types_string))
def array_function_errmsg_formatter(public_api, types):
""" Format the error message for when __array_ufunc__ gives up. """
func_name = '{}.{}'.format(public_api.__module__, public_api.__name__)
return ("no implementation found for '{}' on types that implement "
'__array_function__: {}'.format(func_name, list(types)))
def _ufunc_doc_signature_formatter(ufunc):
"""
Builds a signature string which resembles PEP 457
This is used to construct the first line of the docstring
"""
# input arguments are simple
if ufunc.nin == 1:
in_args = 'x'
else:
in_args = ', '.join(f'x{i+1}' for i in range(ufunc.nin))
# output arguments are both keyword or positional
if ufunc.nout == 0:
out_args = ', /, out=()'
elif ufunc.nout == 1:
out_args = ', /, out=None'
else:
out_args = '[, {positional}], / [, out={default}]'.format(
positional=', '.join(
'out{}'.format(i+1) for i in range(ufunc.nout)),
default=repr((None,)*ufunc.nout)
)
# keyword only args depend on whether this is a gufunc
kwargs = (
", casting='same_kind'"
", order='K'"
", dtype=None"
", subok=True"
)
# NOTE: gufuncs may or may not support the `axis` parameter
if ufunc.signature is None:
kwargs = f", where=True{kwargs}[, signature, extobj]"
else:
kwargs += "[, signature, extobj, axes, axis]"
# join all the parts together
return '{name}({in_args}{out_args}, *{kwargs})'.format(
name=ufunc.__name__,
in_args=in_args,
out_args=out_args,
kwargs=kwargs
)
def npy_ctypes_check(cls):
# determine if a class comes from ctypes, in order to work around
# a bug in the buffer protocol for those objects, bpo-10746
try:
# ctypes class are new-style, so have an __mro__. This probably fails
# for ctypes classes with multiple inheritance.
if IS_PYPY:
# (..., _ctypes.basics._CData, Bufferable, object)
ctype_base = cls.__mro__[-3]
else:
# # (..., _ctypes._CData, object)
ctype_base = cls.__mro__[-2]
# right now, they're part of the _ctypes module
return '_ctypes' in ctype_base.__module__
except Exception:
return False
class recursive:
'''
A decorator class for recursive nested functions.
Naive recursive nested functions hold a reference to themselves:
def outer(*args):
def stringify_leaky(arg0, *arg1):
if len(arg1) > 0:
return stringify_leaky(*arg1) # <- HERE
return str(arg0)
stringify_leaky(*args)
This design pattern creates a reference cycle that is difficult for a
garbage collector to resolve. The decorator class prevents the
cycle by passing the nested function in as an argument `self`:
def outer(*args):
@recursive
def stringify(self, arg0, *arg1):
if len(arg1) > 0:
return self(*arg1)
return str(arg0)
stringify(*args)
'''
def __init__(self, func):
self.func = func
def __call__(self, *args, **kwargs):
return self.func(self, *args, **kwargs)

35
.venv/lib/python3.9/site-packages/numpy/core/_internal.pyi Normal file
View File

@@ -0,0 +1,35 @@
from typing import Any, TypeVar, Type, overload, Optional, Generic
import ctypes as ct
from numpy import ndarray
_CastT = TypeVar("_CastT", bound=ct._CanCastTo) # Copied from `ctypes.cast`
_CT = TypeVar("_CT", bound=ct._CData)
_PT = TypeVar("_PT", bound=Optional[int])
# TODO: Let the likes of `shape_as` and `strides_as` return `None`
# for 0D arrays once we've got shape-support
class _ctypes(Generic[_PT]):
@overload
def __new__(cls, array: ndarray[Any, Any], ptr: None = ...) -> _ctypes[None]: ...
@overload
def __new__(cls, array: ndarray[Any, Any], ptr: _PT) -> _ctypes[_PT]: ...
# NOTE: In practice `shape` and `strides` return one of the concrete
# platform dependant array-types (`c_int`, `c_long` or `c_longlong`)
# corresponding to C's `int_ptr_t`, as determined by `_getintp_ctype`
# TODO: Hook this in to the mypy plugin so that a more appropiate
# `ctypes._SimpleCData[int]` sub-type can be returned
@property
def data(self) -> _PT: ...
@property
def shape(self) -> ct.Array[ct.c_int64]: ...
@property
def strides(self) -> ct.Array[ct.c_int64]: ...
@property
def _as_parameter_(self) -> ct.c_void_p: ...
def data_as(self, obj: Type[_CastT]) -> _CastT: ...
def shape_as(self, obj: Type[_CT]) -> ct.Array[_CT]: ...
def strides_as(self, obj: Type[_CT]) -> ct.Array[_CT]: ...

290
.venv/lib/python3.9/site-packages/numpy/core/_methods.py Normal file
View File

@@ -0,0 +1,290 @@
"""
Array methods which are called by both the C-code for the method
and the Python code for the NumPy-namespace function
"""
import warnings
from contextlib import nullcontext
from numpy.core import multiarray as mu
from numpy.core import umath as um
from numpy.core.multiarray import asanyarray
from numpy.core import numerictypes as nt
from numpy.core import _exceptions
from numpy._globals import _NoValue
from numpy.compat import pickle, os_fspath
# save those O(100) nanoseconds!
umr_maximum = um.maximum.reduce
umr_minimum = um.minimum.reduce
umr_sum = um.add.reduce
umr_prod = um.multiply.reduce
umr_any = um.logical_or.reduce
umr_all = um.logical_and.reduce
# Complex types to -> (2,)float view for fast-path computation in _var()
_complex_to_float = {
nt.dtype(nt.csingle) : nt.dtype(nt.single),
nt.dtype(nt.cdouble) : nt.dtype(nt.double),
}
# Special case for windows: ensure double takes precedence
if nt.dtype(nt.longdouble) != nt.dtype(nt.double):
_complex_to_float.update({
nt.dtype(nt.clongdouble) : nt.dtype(nt.longdouble),
})
# avoid keyword arguments to speed up parsing, saves about 15%-20% for very
# small reductions
def _amax(a, axis=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_maximum(a, axis, None, out, keepdims, initial, where)
def _amin(a, axis=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_minimum(a, axis, None, out, keepdims, initial, where)
def _sum(a, axis=None, dtype=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
def _prod(a, axis=None, dtype=None, out=None, keepdims=False,
initial=_NoValue, where=True):
return umr_prod(a, axis, dtype, out, keepdims, initial, where)
def _any(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
# Parsing keyword arguments is currently fairly slow, so avoid it for now
if where is True:
return umr_any(a, axis, dtype, out, keepdims)
return umr_any(a, axis, dtype, out, keepdims, where=where)
def _all(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
# Parsing keyword arguments is currently fairly slow, so avoid it for now
if where is True:
return umr_all(a, axis, dtype, out, keepdims)
return umr_all(a, axis, dtype, out, keepdims, where=where)
def _count_reduce_items(arr, axis, keepdims=False, where=True):
# fast-path for the default case
if where is True:
# no boolean mask given, calculate items according to axis
if axis is None:
axis = tuple(range(arr.ndim))
elif not isinstance(axis, tuple):
axis = (axis,)
items = nt.intp(1)
for ax in axis:
items *= arr.shape[mu.normalize_axis_index(ax, arr.ndim)]
else:
# TODO: Optimize case when `where` is broadcast along a non-reduction
# axis and full sum is more excessive than needed.
# guarded to protect circular imports
from numpy.lib.stride_tricks import broadcast_to
# count True values in (potentially broadcasted) boolean mask
items = umr_sum(broadcast_to(where, arr.shape), axis, nt.intp, None,
keepdims)
return items
# Numpy 1.17.0, 2019-02-24
# Various clip behavior deprecations, marked with _clip_dep as a prefix.
def _clip_dep_is_scalar_nan(a):
# guarded to protect circular imports
from numpy.core.fromnumeric import ndim
if ndim(a) != 0:
return False
try:
return um.isnan(a)
except TypeError:
return False
def _clip_dep_is_byte_swapped(a):
if isinstance(a, mu.ndarray):
return not a.dtype.isnative
return False
def _clip_dep_invoke_with_casting(ufunc, *args, out=None, casting=None, **kwargs):
# normal path
if casting is not None:
return ufunc(*args, out=out, casting=casting, **kwargs)
# try to deal with broken casting rules
try:
return ufunc(*args, out=out, **kwargs)
except _exceptions._UFuncOutputCastingError as e:
# Numpy 1.17.0, 2019-02-24
warnings.warn(
"Converting the output of clip from {!r} to {!r} is deprecated. "
"Pass `casting=\"unsafe\"` explicitly to silence this warning, or "
"correct the type of the variables.".format(e.from_, e.to),
DeprecationWarning,
stacklevel=2
)
return ufunc(*args, out=out, casting="unsafe", **kwargs)
def _clip(a, min=None, max=None, out=None, *, casting=None, **kwargs):
if min is None and max is None:
raise ValueError("One of max or min must be given")
# Numpy 1.17.0, 2019-02-24
# This deprecation probably incurs a substantial slowdown for small arrays,
# it will be good to get rid of it.
if not _clip_dep_is_byte_swapped(a) and not _clip_dep_is_byte_swapped(out):
using_deprecated_nan = False
if _clip_dep_is_scalar_nan(min):
min = -float('inf')
using_deprecated_nan = True
if _clip_dep_is_scalar_nan(max):
max = float('inf')
using_deprecated_nan = True
if using_deprecated_nan:
warnings.warn(
"Passing `np.nan` to mean no clipping in np.clip has always "
"been unreliable, and is now deprecated. "
"In future, this will always return nan, like it already does "
"when min or max are arrays that contain nan. "
"To skip a bound, pass either None or an np.inf of an "
"appropriate sign.",
DeprecationWarning,
stacklevel=2
)
if min is None:
return _clip_dep_invoke_with_casting(
um.minimum, a, max, out=out, casting=casting, **kwargs)
elif max is None:
return _clip_dep_invoke_with_casting(
um.maximum, a, min, out=out, casting=casting, **kwargs)
else:
return _clip_dep_invoke_with_casting(
um.clip, a, min, max, out=out, casting=casting, **kwargs)
def _mean(a, axis=None, dtype=None, out=None, keepdims=False, *, where=True):
arr = asanyarray(a)
is_float16_result = False
rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
if rcount == 0 if where is True else umr_any(rcount == 0, axis=None):
warnings.warn("Mean of empty slice.", RuntimeWarning, stacklevel=2)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None:
if issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
dtype = mu.dtype('f8')
elif issubclass(arr.dtype.type, nt.float16):
dtype = mu.dtype('f4')
is_float16_result = True
ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
if isinstance(ret, mu.ndarray):
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
if is_float16_result and out is None:
ret = arr.dtype.type(ret)
elif hasattr(ret, 'dtype'):
if is_float16_result:
ret = arr.dtype.type(ret / rcount)
else:
ret = ret.dtype.type(ret / rcount)
else:
ret = ret / rcount
return ret
def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *,
where=True):
arr = asanyarray(a)
rcount = _count_reduce_items(arr, axis, keepdims=keepdims, where=where)
# Make this warning show up on top.
if ddof >= rcount if where is True else umr_any(ddof >= rcount, axis=None):
warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning,
stacklevel=2)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
dtype = mu.dtype('f8')
# Compute the mean.
# Note that if dtype is not of inexact type then arraymean will
# not be either.
arrmean = umr_sum(arr, axis, dtype, keepdims=True, where=where)
# The shape of rcount has to match arrmean to not change the shape of out
# in broadcasting. Otherwise, it cannot be stored back to arrmean.
if rcount.ndim == 0:
# fast-path for default case when where is True
div = rcount
else:
# matching rcount to arrmean when where is specified as array
div = rcount.reshape(arrmean.shape)
if isinstance(arrmean, mu.ndarray):
arrmean = um.true_divide(arrmean, div, out=arrmean, casting='unsafe',
subok=False)
else:
arrmean = arrmean.dtype.type(arrmean / rcount)
# Compute sum of squared deviations from mean
# Note that x may not be inexact and that we need it to be an array,
# not a scalar.
x = asanyarray(arr - arrmean)
if issubclass(arr.dtype.type, (nt.floating, nt.integer)):
x = um.multiply(x, x, out=x)
# Fast-paths for built-in complex types
elif x.dtype in _complex_to_float:
xv = x.view(dtype=(_complex_to_float[x.dtype], (2,)))
um.multiply(xv, xv, out=xv)
x = um.add(xv[..., 0], xv[..., 1], out=x.real).real
# Most general case; includes handling object arrays containing imaginary
# numbers and complex types with non-native byteorder
else:
x = um.multiply(x, um.conjugate(x), out=x).real
ret = umr_sum(x, axis, dtype, out, keepdims=keepdims, where=where)
# Compute degrees of freedom and make sure it is not negative.
rcount = um.maximum(rcount - ddof, 0)
# divide by degrees of freedom
if isinstance(ret, mu.ndarray):
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
elif hasattr(ret, 'dtype'):
ret = ret.dtype.type(ret / rcount)
else:
ret = ret / rcount
return ret
def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *,
where=True):
ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
keepdims=keepdims, where=where)
if isinstance(ret, mu.ndarray):
ret = um.sqrt(ret, out=ret)
elif hasattr(ret, 'dtype'):
ret = ret.dtype.type(um.sqrt(ret))
else:
ret = um.sqrt(ret)
return ret
def _ptp(a, axis=None, out=None, keepdims=False):
return um.subtract(
umr_maximum(a, axis, None, out, keepdims),
umr_minimum(a, axis, None, None, keepdims),
out
)
def _dump(self, file, protocol=2):
if hasattr(file, 'write'):
ctx = nullcontext(file)
else:
ctx = open(os_fspath(file), "wb")
with ctx as f:
pickle.dump(self, f, protocol=protocol)
def _dumps(self, protocol=2):
return pickle.dumps(self, protocol=protocol)

BIN
.venv/lib/python3.9/site-packages/numpy/core/_multiarray_tests.cpython-39-x86_64-linux-gnu.so Executable file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/_multiarray_umath.cpython-39-x86_64-linux-gnu.so Executable file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/_operand_flag_tests.cpython-39-x86_64-linux-gnu.so Executable file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/_rational_tests.cpython-39-x86_64-linux-gnu.so Executable file
View File

Binary file not shown.

BIN
.venv/lib/python3.9/site-packages/numpy/core/_simd.cpython-39-x86_64-linux-gnu.so Executable file
View File

Binary file not shown.

100
.venv/lib/python3.9/site-packages/numpy/core/_string_helpers.py Normal file
View File

@@ -0,0 +1,100 @@
"""
String-handling utilities to avoid locale-dependence.
Used primarily to generate type name aliases.
"""
# "import string" is costly to import!
# Construct the translation tables directly
# "A" = chr(65), "a" = chr(97)
_all_chars = [chr(_m) for _m in range(256)]
_ascii_upper = _all_chars[65:65+26]
_ascii_lower = _all_chars[97:97+26]
LOWER_TABLE = "".join(_all_chars[:65] + _ascii_lower + _all_chars[65+26:])
UPPER_TABLE = "".join(_all_chars[:97] + _ascii_upper + _all_chars[97+26:])
def english_lower(s):
""" Apply English case rules to convert ASCII strings to all lower case.
This is an internal utility function to replace calls to str.lower() such
that we can avoid changing behavior with changing locales. In particular,
Turkish has distinct dotted and dotless variants of the Latin letter "I" in
both lowercase and uppercase. Thus, "I".lower() != "i" in a "tr" locale.
Parameters
----------
s : str
Returns
-------
lowered : str
Examples
--------
>>> from numpy.core.numerictypes import english_lower
>>> english_lower('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789_')
'abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz0123456789_'
>>> english_lower('')
''
"""
lowered = s.translate(LOWER_TABLE)
return lowered
def english_upper(s):
""" Apply English case rules to convert ASCII strings to all upper case.
This is an internal utility function to replace calls to str.upper() such
that we can avoid changing behavior with changing locales. In particular,
Turkish has distinct dotted and dotless variants of the Latin letter "I" in
both lowercase and uppercase. Thus, "i".upper() != "I" in a "tr" locale.
Parameters
----------
s : str
Returns
-------
uppered : str
Examples
--------
>>> from numpy.core.numerictypes import english_upper
>>> english_upper('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789_')
'ABCDEFGHIJKLMNOPQRSTUVWXYZABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_'
>>> english_upper('')
''
"""
uppered = s.translate(UPPER_TABLE)
return uppered
def english_capitalize(s):
""" Apply English case rules to convert the first character of an ASCII
string to upper case.
This is an internal utility function to replace calls to str.capitalize()
such that we can avoid changing behavior with changing locales.
Parameters
----------
s : str
Returns
-------
capitalized : str
Examples
--------
>>> from numpy.core.numerictypes import english_capitalize
>>> english_capitalize('int8')
'Int8'
>>> english_capitalize('Int8')
'Int8'
>>> english_capitalize('')
''
"""
if s:
return english_upper(s[0]) + s[1:]
else:
return s

BIN
.venv/lib/python3.9/site-packages/numpy/core/_struct_ufunc_tests.cpython-39-x86_64-linux-gnu.so Executable file
View File

Binary file not shown.

244
.venv/lib/python3.9/site-packages/numpy/core/_type_aliases.py Normal file
View File

@@ -0,0 +1,244 @@
"""
Due to compatibility, numpy has a very large number of different naming
conventions for the scalar types (those subclassing from `numpy.generic`).
This file produces a convoluted set of dictionaries mapping names to types,
and sometimes other mappings too.
.. data:: allTypes
A dictionary of names to types that will be exposed as attributes through
``np.core.numerictypes.*``
.. data:: sctypeDict
Similar to `allTypes`, but maps a broader set of aliases to their types.
.. data:: sctypes
A dictionary keyed by a "type group" string, providing a list of types
under that group.
"""
from numpy.compat import unicode
from numpy.core._string_helpers import english_lower
from numpy.core.multiarray import typeinfo, dtype
from numpy.core._dtype import _kind_name
sctypeDict = {} # Contains all leaf-node scalar types with aliases
allTypes = {} # Collect the types we will add to the module
# separate the actual type info from the abstract base classes
_abstract_types = {}
_concrete_typeinfo = {}
for k, v in typeinfo.items():
# make all the keys lowercase too
k = english_lower(k)
if isinstance(v, type):
_abstract_types[k] = v
else:
_concrete_typeinfo[k] = v
_concrete_types = {v.type for k, v in _concrete_typeinfo.items()}
def _bits_of(obj):
try:
info = next(v for v in _concrete_typeinfo.values() if v.type is obj)
except StopIteration:
if obj in _abstract_types.values():
msg = "Cannot count the bits of an abstract type"
raise ValueError(msg) from None
# some third-party type - make a best-guess
return dtype(obj).itemsize * 8
else:
return info.bits
def bitname(obj):
"""Return a bit-width name for a given type object"""
bits = _bits_of(obj)
dt = dtype(obj)
char = dt.kind
base = _kind_name(dt)
if base == 'object':
bits = 0
if bits != 0:
char = "%s%d" % (char, bits // 8)
return base, bits, char
def _add_types():
for name, info in _concrete_typeinfo.items():
# define C-name and insert typenum and typechar references also
allTypes[name] = info.type
sctypeDict[name] = info.type
sctypeDict[info.char] = info.type
sctypeDict[info.num] = info.type
for name, cls in _abstract_types.items():
allTypes[name] = cls
_add_types()
# This is the priority order used to assign the bit-sized NPY_INTxx names, which
# must match the order in npy_common.h in order for NPY_INTxx and np.intxx to be
# consistent.
# If two C types have the same size, then the earliest one in this list is used
# as the sized name.
_int_ctypes = ['long', 'longlong', 'int', 'short', 'byte']
_uint_ctypes = list('u' + t for t in _int_ctypes)
def _add_aliases():
for name, info in _concrete_typeinfo.items():
# these are handled by _add_integer_aliases
if name in _int_ctypes or name in _uint_ctypes:
continue
# insert bit-width version for this class (if relevant)
base, bit, char = bitname(info.type)
myname = "%s%d" % (base, bit)
# ensure that (c)longdouble does not overwrite the aliases assigned to
# (c)double
if name in ('longdouble', 'clongdouble') and myname in allTypes:
continue
allTypes[myname] = info.type
# add mapping for both the bit name and the numarray name
sctypeDict[myname] = info.type
# add forward, reverse, and string mapping to numarray
sctypeDict[char] = info.type
# Add deprecated numeric-style type aliases manually, at some point
# we may want to deprecate the lower case "bytes0" version as well.
for name in ["Bytes0", "Datetime64", "Str0", "Uint32", "Uint64"]:
if english_lower(name) not in allTypes:
# Only one of Uint32 or Uint64, aliases of `np.uintp`, was (and is) defined, note that this
# is not UInt32/UInt64 (capital i), which is removed.
continue
allTypes[name] = allTypes[english_lower(name)]
sctypeDict[name] = sctypeDict[english_lower(name)]
_add_aliases()
def _add_integer_aliases():
seen_bits = set()
for i_ctype, u_ctype in zip(_int_ctypes, _uint_ctypes):
i_info = _concrete_typeinfo[i_ctype]
u_info = _concrete_typeinfo[u_ctype]
bits = i_info.bits # same for both
for info, charname, intname in [
(i_info,'i%d' % (bits//8,), 'int%d' % bits),
(u_info,'u%d' % (bits//8,), 'uint%d' % bits)]:
if bits not in seen_bits:
# sometimes two different types have the same number of bits
# if so, the one iterated over first takes precedence
allTypes[intname] = info.type
sctypeDict[intname] = info.type
sctypeDict[charname] = info.type
seen_bits.add(bits)
_add_integer_aliases()
# We use these later
void = allTypes['void']
#
# Rework the Python names (so that float and complex and int are consistent
# with Python usage)
#
def _set_up_aliases():
type_pairs = [('complex_', 'cdouble'),
('int0', 'intp'),
('uint0', 'uintp'),
('single', 'float'),
('csingle', 'cfloat'),
('singlecomplex', 'cfloat'),
('float_', 'double'),
('intc', 'int'),
('uintc', 'uint'),
('int_', 'long'),
('uint', 'ulong'),
('cfloat', 'cdouble'),
('longfloat', 'longdouble'),
('clongfloat', 'clongdouble'),
('longcomplex', 'clongdouble'),
('bool_', 'bool'),
('bytes_', 'string'),
('string_', 'string'),
('str_', 'unicode'),
('unicode_', 'unicode'),
('object_', 'object')]
for alias, t in type_pairs:
allTypes[alias] = allTypes[t]
sctypeDict[alias] = sctypeDict[t]
# Remove aliases overriding python types and modules
to_remove = ['ulong', 'object', 'int', 'float',
'complex', 'bool', 'string', 'datetime', 'timedelta',
'bytes', 'str']
for t in to_remove:
try:
del allTypes[t]
del sctypeDict[t]
except KeyError:
pass
_set_up_aliases()
sctypes = {'int': [],
'uint':[],
'float':[],
'complex':[],
'others':[bool, object, bytes, unicode, void]}
def _add_array_type(typename, bits):
try:
t = allTypes['%s%d' % (typename, bits)]
except KeyError:
pass
else:
sctypes[typename].append(t)
def _set_array_types():
ibytes = [1, 2, 4, 8, 16, 32, 64]
fbytes = [2, 4, 8, 10, 12, 16, 32, 64]
for bytes in ibytes:
bits = 8*bytes
_add_array_type('int', bits)
_add_array_type('uint', bits)
for bytes in fbytes:
bits = 8*bytes
_add_array_type('float', bits)
_add_array_type('complex', 2*bits)
_gi = dtype('p')
if _gi.type not in sctypes['int']:
indx = 0
sz = _gi.itemsize
_lst = sctypes['int']
while (indx < len(_lst) and sz >= _lst[indx](0).itemsize):
indx += 1
sctypes['int'].insert(indx, _gi.type)
sctypes['uint'].insert(indx, dtype('P').type)
_set_array_types()
# Add additional strings to the sctypeDict
_toadd = ['int', 'float', 'complex', 'bool', 'object',
'str', 'bytes', ('a', 'bytes_')]
for name in _toadd:
if isinstance(name, tuple):
sctypeDict[name[0]] = allTypes[name[1]]
else:
sctypeDict[name] = allTypes['%s_' % name]
del _toadd, name

19
.venv/lib/python3.9/site-packages/numpy/core/_type_aliases.pyi Normal file
View File

@@ -0,0 +1,19 @@
import sys
from typing import Dict, Union, Type, List
from numpy import generic, signedinteger, unsignedinteger, floating, complexfloating
if sys.version_info >= (3, 8):
from typing import TypedDict
else:
from typing_extensions import TypedDict
class _SCTypes(TypedDict):
int: List[Type[signedinteger]]
uint: List[Type[unsignedinteger]]
float: List[Type[floating]]
complex: List[Type[complexfloating]]
others: List[type]
sctypeDict: Dict[Union[int, str], Type[generic]]
sctypes: _SCTypes

446
.venv/lib/python3.9/site-packages/numpy/core/_ufunc_config.py Normal file
View File

@@ -0,0 +1,446 @@
"""
Functions for changing global ufunc configuration
This provides helpers which wrap `umath.geterrobj` and `umath.seterrobj`
"""
import collections.abc
import contextlib
from .overrides import set_module
from .umath import (
UFUNC_BUFSIZE_DEFAULT,
ERR_IGNORE, ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT, ERR_LOG, ERR_DEFAULT,
SHIFT_DIVIDEBYZERO, SHIFT_OVERFLOW, SHIFT_UNDERFLOW, SHIFT_INVALID,
)
from . import umath
__all__ = [
"seterr", "geterr", "setbufsize", "getbufsize", "seterrcall", "geterrcall",
"errstate",
]
_errdict = {"ignore": ERR_IGNORE,
"warn": ERR_WARN,
"raise": ERR_RAISE,
"call": ERR_CALL,
"print": ERR_PRINT,
"log": ERR_LOG}
_errdict_rev = {value: key for key, value in _errdict.items()}
@set_module('numpy')
def seterr(all=None, divide=None, over=None, under=None, invalid=None):
"""
Set how floating-point errors are handled.
Note that operations on integer scalar types (such as `int16`) are
handled like floating point, and are affected by these settings.
Parameters
----------
all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Set treatment for all types of floating-point errors at once:
- ignore: Take no action when the exception occurs.
- warn: Print a `RuntimeWarning` (via the Python `warnings` module).
- raise: Raise a `FloatingPointError`.
- call: Call a function specified using the `seterrcall` function.
- print: Print a warning directly to ``stdout``.
- log: Record error in a Log object specified by `seterrcall`.
The default is not to change the current behavior.
divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for division by zero.
over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for floating-point overflow.
under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for floating-point underflow.
invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for invalid floating-point operation.
Returns
-------
old_settings : dict
Dictionary containing the old settings.
See also
--------
seterrcall : Set a callback function for the 'call' mode.
geterr, geterrcall, errstate
Notes
-----
The floating-point exceptions are defined in the IEEE 754 standard [1]_:
- Division by zero: infinite result obtained from finite numbers.
- Overflow: result too large to be expressed.
- Underflow: result so close to zero that some precision
was lost.
- Invalid operation: result is not an expressible number, typically
indicates that a NaN was produced.
.. [1] https://en.wikipedia.org/wiki/IEEE_754
Examples
--------
>>> old_settings = np.seterr(all='ignore') #seterr to known value
>>> np.seterr(over='raise')
{'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
>>> np.seterr(**old_settings) # reset to default
{'divide': 'ignore', 'over': 'raise', 'under': 'ignore', 'invalid': 'ignore'}
>>> np.int16(32000) * np.int16(3)
30464
>>> old_settings = np.seterr(all='warn', over='raise')
>>> np.int16(32000) * np.int16(3)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
FloatingPointError: overflow encountered in short_scalars
>>> old_settings = np.seterr(all='print')
>>> np.geterr()
{'divide': 'print', 'over': 'print', 'under': 'print', 'invalid': 'print'}
>>> np.int16(32000) * np.int16(3)
30464
"""
pyvals = umath.geterrobj()
old = geterr()
if divide is None:
divide = all or old['divide']
if over is None:
over = all or old['over']
if under is None:
under = all or old['under']
if invalid is None:
invalid = all or old['invalid']
maskvalue = ((_errdict[divide] << SHIFT_DIVIDEBYZERO) +
(_errdict[over] << SHIFT_OVERFLOW) +
(_errdict[under] << SHIFT_UNDERFLOW) +
(_errdict[invalid] << SHIFT_INVALID))
pyvals[1] = maskvalue
umath.seterrobj(pyvals)
return old
@set_module('numpy')
def geterr():
"""
Get the current way of handling floating-point errors.
Returns
-------
res : dict
A dictionary with keys "divide", "over", "under", and "invalid",
whose values are from the strings "ignore", "print", "log", "warn",
"raise", and "call". The keys represent possible floating-point
exceptions, and the values define how these exceptions are handled.
See Also
--------
geterrcall, seterr, seterrcall
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> np.geterr()
{'divide': 'warn', 'over': 'warn', 'under': 'ignore', 'invalid': 'warn'}
>>> np.arange(3.) / np.arange(3.)
array([nan, 1., 1.])
>>> oldsettings = np.seterr(all='warn', over='raise')
>>> np.geterr()
{'divide': 'warn', 'over': 'raise', 'under': 'warn', 'invalid': 'warn'}
>>> np.arange(3.) / np.arange(3.)
array([nan, 1., 1.])
"""
maskvalue = umath.geterrobj()[1]
mask = 7
res = {}
val = (maskvalue >> SHIFT_DIVIDEBYZERO) & mask
res['divide'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_OVERFLOW) & mask
res['over'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_UNDERFLOW) & mask
res['under'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_INVALID) & mask
res['invalid'] = _errdict_rev[val]
return res
@set_module('numpy')
def setbufsize(size):
"""
Set the size of the buffer used in ufuncs.
Parameters
----------
size : int
Size of buffer.
"""
if size > 10e6:
raise ValueError("Buffer size, %s, is too big." % size)
if size < 5:
raise ValueError("Buffer size, %s, is too small." % size)
if size % 16 != 0:
raise ValueError("Buffer size, %s, is not a multiple of 16." % size)
pyvals = umath.geterrobj()
old = getbufsize()
pyvals[0] = size
umath.seterrobj(pyvals)
return old
@set_module('numpy')
def getbufsize():
"""
Return the size of the buffer used in ufuncs.
Returns
-------
getbufsize : int
Size of ufunc buffer in bytes.
"""
return umath.geterrobj()[0]
@set_module('numpy')
def seterrcall(func):
"""
Set the floating-point error callback function or log object.
There are two ways to capture floating-point error messages. The first
is to set the error-handler to 'call', using `seterr`. Then, set
the function to call using this function.
The second is to set the error-handler to 'log', using `seterr`.
Floating-point errors then trigger a call to the 'write' method of
the provided object.
Parameters
----------
func : callable f(err, flag) or object with write method
Function to call upon floating-point errors ('call'-mode) or
object whose 'write' method is used to log such message ('log'-mode).
The call function takes two arguments. The first is a string describing
the type of error (such as "divide by zero", "overflow", "underflow",
or "invalid value"), and the second is the status flag. The flag is a
byte, whose four least-significant bits indicate the type of error, one
of "divide", "over", "under", "invalid"::
[0 0 0 0 divide over under invalid]
In other words, ``flags = divide + 2*over + 4*under + 8*invalid``.
If an object is provided, its write method should take one argument,
a string.
Returns
-------
h : callable, log instance or None
The old error handler.
See Also
--------
seterr, geterr, geterrcall
Examples
--------
Callback upon error:
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
...
>>> saved_handler = np.seterrcall(err_handler)
>>> save_err = np.seterr(all='call')
>>> np.array([1, 2, 3]) / 0.0
Floating point error (divide by zero), with flag 1
array([inf, inf, inf])
>>> np.seterrcall(saved_handler)
<function err_handler at 0x...>
>>> np.seterr(**save_err)
{'divide': 'call', 'over': 'call', 'under': 'call', 'invalid': 'call'}
Log error message:
>>> class Log:
... def write(self, msg):
... print("LOG: %s" % msg)
...
>>> log = Log()
>>> saved_handler = np.seterrcall(log)
>>> save_err = np.seterr(all='log')
>>> np.array([1, 2, 3]) / 0.0
LOG: Warning: divide by zero encountered in true_divide
array([inf, inf, inf])
>>> np.seterrcall(saved_handler)
<numpy.core.numeric.Log object at 0x...>
>>> np.seterr(**save_err)
{'divide': 'log', 'over': 'log', 'under': 'log', 'invalid': 'log'}
"""
if func is not None and not isinstance(func, collections.abc.Callable):
if (not hasattr(func, 'write') or
not isinstance(func.write, collections.abc.Callable)):
raise ValueError("Only callable can be used as callback")
pyvals = umath.geterrobj()
old = geterrcall()
pyvals[2] = func
umath.seterrobj(pyvals)
return old
@set_module('numpy')
def geterrcall():
"""
Return the current callback function used on floating-point errors.
When the error handling for a floating-point error (one of "divide",
"over", "under", or "invalid") is set to 'call' or 'log', the function
that is called or the log instance that is written to is returned by
`geterrcall`. This function or log instance has been set with
`seterrcall`.
Returns
-------
errobj : callable, log instance or None
The current error handler. If no handler was set through `seterrcall`,
``None`` is returned.
See Also
--------
seterrcall, seterr, geterr
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> np.geterrcall() # we did not yet set a handler, returns None
>>> oldsettings = np.seterr(all='call')
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
>>> oldhandler = np.seterrcall(err_handler)
>>> np.array([1, 2, 3]) / 0.0
Floating point error (divide by zero), with flag 1
array([inf, inf, inf])
>>> cur_handler = np.geterrcall()
>>> cur_handler is err_handler
True
"""
return umath.geterrobj()[2]
class _unspecified:
pass
_Unspecified = _unspecified()
@set_module('numpy')
class errstate(contextlib.ContextDecorator):
"""
errstate(**kwargs)
Context manager for floating-point error handling.
Using an instance of `errstate` as a context manager allows statements in
that context to execute with a known error handling behavior. Upon entering
the context the error handling is set with `seterr` and `seterrcall`, and
upon exiting it is reset to what it was before.
.. versionchanged:: 1.17.0
`errstate` is also usable as a function decorator, saving
a level of indentation if an entire function is wrapped.
See :py:class:`contextlib.ContextDecorator` for more information.
Parameters
----------
kwargs : {divide, over, under, invalid}
Keyword arguments. The valid keywords are the possible floating-point
exceptions. Each keyword should have a string value that defines the
treatment for the particular error. Possible values are
{'ignore', 'warn', 'raise', 'call', 'print', 'log'}.
See Also
--------
seterr, geterr, seterrcall, geterrcall
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> olderr = np.seterr(all='ignore') # Set error handling to known state.
>>> np.arange(3) / 0.
array([nan, inf, inf])
>>> with np.errstate(divide='warn'):
... np.arange(3) / 0.
array([nan, inf, inf])
>>> np.sqrt(-1)
nan
>>> with np.errstate(invalid='raise'):
... np.sqrt(-1)
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
FloatingPointError: invalid value encountered in sqrt
Outside the context the error handling behavior has not changed:
>>> np.geterr()
{'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}
"""
def __init__(self, *, call=_Unspecified, **kwargs):
self.call = call
self.kwargs = kwargs
def __enter__(self):
self.oldstate = seterr(**self.kwargs)
if self.call is not _Unspecified:
self.oldcall = seterrcall(self.call)
def __exit__(self, *exc_info):
seterr(**self.oldstate)
if self.call is not _Unspecified:
seterrcall(self.oldcall)
def _setdef():
defval = [UFUNC_BUFSIZE_DEFAULT, ERR_DEFAULT, None]
umath.seterrobj(defval)
# set the default values
_setdef()

43
.venv/lib/python3.9/site-packages/numpy/core/_ufunc_config.pyi Normal file
View File

@@ -0,0 +1,43 @@
import sys
from typing import Optional, Union, Callable, Any
if sys.version_info >= (3, 8):
from typing import Literal, Protocol, TypedDict
else:
from typing_extensions import Literal, Protocol, TypedDict
_ErrKind = Literal["ignore", "warn", "raise", "call", "print", "log"]
_ErrFunc = Callable[[str, int], Any]
class _SupportsWrite(Protocol):
def write(self, __msg: str) -> Any: ...
class _ErrDict(TypedDict):
divide: _ErrKind
over: _ErrKind
under: _ErrKind
invalid: _ErrKind
class _ErrDictOptional(TypedDict, total=False):
all: Optional[_ErrKind]
divide: Optional[_ErrKind]
over: Optional[_ErrKind]
under: Optional[_ErrKind]
invalid: Optional[_ErrKind]
def seterr(
all: Optional[_ErrKind] = ...,
divide: Optional[_ErrKind] = ...,
over: Optional[_ErrKind] = ...,
under: Optional[_ErrKind] = ...,
invalid: Optional[_ErrKind] = ...,
) -> _ErrDict: ...
def geterr() -> _ErrDict: ...
def setbufsize(size: int) -> int: ...
def getbufsize() -> int: ...
def seterrcall(
func: Union[None, _ErrFunc, _SupportsWrite]
) -> Union[None, _ErrFunc, _SupportsWrite]: ...
def geterrcall() -> Union[None, _ErrFunc, _SupportsWrite]: ...
# See `numpy/__init__.pyi` for the `errstate` class

BIN
.venv/lib/python3.9/site-packages/numpy/core/_umath_tests.cpython-39-x86_64-linux-gnu.so Executable file
View File

Binary file not shown.

1664
.venv/lib/python3.9/site-packages/numpy/core/arrayprint.py Normal file
View File

File diff suppressed because it is too large Load Diff

147
.venv/lib/python3.9/site-packages/numpy/core/arrayprint.pyi Normal file
View File

@@ -0,0 +1,147 @@
import sys
from types import TracebackType
from typing import Any, Optional, Callable, Union, Type
# Using a private class is by no means ideal, but it is simply a consquence
# of a `contextlib.context` returning an instance of aformentioned class
from contextlib import _GeneratorContextManager
from numpy import (
ndarray,
generic,
bool_,
integer,
timedelta64,
datetime64,
floating,
complexfloating,
void,
str_,
bytes_,
longdouble,
clongdouble,
)
from numpy.typing import ArrayLike, _CharLike_co, _FloatLike_co
if sys.version_info > (3, 8):
from typing import Literal, TypedDict, SupportsIndex
else:
from typing_extensions import Literal, TypedDict, SupportsIndex
_FloatMode = Literal["fixed", "unique", "maxprec", "maxprec_equal"]
class _FormatDict(TypedDict, total=False):
bool: Callable[[bool_], str]
int: Callable[[integer[Any]], str]
timedelta: Callable[[timedelta64], str]
datetime: Callable[[datetime64], str]
float: Callable[[floating[Any]], str]
longfloat: Callable[[longdouble], str]
complexfloat: Callable[[complexfloating[Any, Any]], str]
longcomplexfloat: Callable[[clongdouble], str]
void: Callable[[void], str]
numpystr: Callable[[_CharLike_co], str]
object: Callable[[object], str]
all: Callable[[object], str]
int_kind: Callable[[integer[Any]], str]
float_kind: Callable[[floating[Any]], str]
complex_kind: Callable[[complexfloating[Any, Any]], str]
str_kind: Callable[[_CharLike_co], str]
class _FormatOptions(TypedDict):
precision: int
threshold: int
edgeitems: int
linewidth: int
suppress: bool
nanstr: str
infstr: str
formatter: Optional[_FormatDict]
sign: Literal["-", "+", " "]
floatmode: _FloatMode
legacy: Literal[False, "1.13"]
def set_printoptions(
precision: Optional[SupportsIndex] = ...,
threshold: Optional[int] = ...,
edgeitems: Optional[int] = ...,
linewidth: Optional[int] = ...,
suppress: Optional[bool] = ...,
nanstr: Optional[str] = ...,
infstr: Optional[str] = ...,
formatter: Optional[_FormatDict] = ...,
sign: Optional[Literal["-", "+", " "]] = ...,
floatmode: Optional[_FloatMode] = ...,
*,
legacy: Optional[Literal[False, "1.13"]] = ...
) -> None: ...
def get_printoptions() -> _FormatOptions: ...
def array2string(
a: ndarray[Any, Any],
max_line_width: Optional[int] = ...,
precision: Optional[SupportsIndex] = ...,
suppress_small: Optional[bool] = ...,
separator: str = ...,
prefix: str = ...,
# NOTE: With the `style` argument being deprecated,
# all arguments between `formatter` and `suffix` are de facto
# keyworld-only arguments
*,
formatter: Optional[_FormatDict] = ...,
threshold: Optional[int] = ...,
edgeitems: Optional[int] = ...,
sign: Optional[Literal["-", "+", " "]] = ...,
floatmode: Optional[_FloatMode] = ...,
suffix: str = ...,
legacy: Optional[Literal[False, "1.13"]] = ...,
) -> str: ...
def format_float_scientific(
x: _FloatLike_co,
precision: Optional[int] = ...,
unique: bool = ...,
trim: Literal["k", ".", "0", "-"] = ...,
sign: bool = ...,
pad_left: Optional[int] = ...,
exp_digits: Optional[int] = ...,
min_digits: Optional[int] = ...,
) -> str: ...
def format_float_positional(
x: _FloatLike_co,
precision: Optional[int] = ...,
unique: bool = ...,
fractional: bool = ...,
trim: Literal["k", ".", "0", "-"] = ...,
sign: bool = ...,
pad_left: Optional[int] = ...,
pad_right: Optional[int] = ...,
min_digits: Optional[int] = ...,
) -> str: ...
def array_repr(
arr: ndarray[Any, Any],
max_line_width: Optional[int] = ...,
precision: Optional[SupportsIndex] = ...,
suppress_small: Optional[bool] = ...,
) -> str: ...
def array_str(
a: ndarray[Any, Any],
max_line_width: Optional[int] = ...,
precision: Optional[SupportsIndex] = ...,
suppress_small: Optional[bool] = ...,
) -> str: ...
def set_string_function(
f: Optional[Callable[[ndarray[Any, Any]], str]], repr: bool = ...
) -> None: ...
def printoptions(
precision: Optional[SupportsIndex] = ...,
threshold: Optional[int] = ...,
edgeitems: Optional[int] = ...,
linewidth: Optional[int] = ...,
suppress: Optional[bool] = ...,
nanstr: Optional[str] = ...,
infstr: Optional[str] = ...,
formatter: Optional[_FormatDict] = ...,
sign: Optional[Literal["-", "+", " "]] = ...,
floatmode: Optional[_FloatMode] = ...,
*,
legacy: Optional[Literal[False, "1.13"]] = ...
) -> _GeneratorContextManager[_FormatOptions]: ...

13
.venv/lib/python3.9/site-packages/numpy/core/cversions.py Normal file
View File

@@ -0,0 +1,13 @@
"""Simple script to compute the api hash of the current API.
The API has is defined by numpy_api_order and ufunc_api_order.
"""
from os.path import dirname
from code_generators.genapi import fullapi_hash
from code_generators.numpy_api import full_api
if __name__ == '__main__':
curdir = dirname(__file__)
print(fullapi_hash(full_api))

2795
.venv/lib/python3.9/site-packages/numpy/core/defchararray.py Normal file
View File

File diff suppressed because it is too large Load Diff

1431
.venv/lib/python3.9/site-packages/numpy/core/einsumfunc.py Normal file
View File

File diff suppressed because it is too large Load Diff

142
.venv/lib/python3.9/site-packages/numpy/core/einsumfunc.pyi Normal file
View File

@@ -0,0 +1,142 @@
import sys
from typing import List, TypeVar, Optional, Any, overload, Union, Tuple, Sequence
from numpy import (
ndarray,
dtype,
bool_,
unsignedinteger,
signedinteger,
floating,
complexfloating,
number,
_OrderKACF,
)
from numpy.typing import (
_ArrayLikeBool_co,
_ArrayLikeUInt_co,
_ArrayLikeInt_co,
_ArrayLikeFloat_co,
_ArrayLikeComplex_co,
_DTypeLikeBool,
_DTypeLikeUInt,
_DTypeLikeInt,
_DTypeLikeFloat,
_DTypeLikeComplex,
_DTypeLikeComplex_co,
)
if sys.version_info >= (3, 8):
from typing import Literal
else:
from typing_extensions import Literal
_ArrayType = TypeVar(
"_ArrayType",
bound=ndarray[Any, dtype[Union[bool_, number[Any]]]],
)
_OptimizeKind = Union[
None, bool, Literal["greedy", "optimal"], Sequence[Any]
]
_CastingSafe = Literal["no", "equiv", "safe", "same_kind"]
_CastingUnsafe = Literal["unsafe"]
__all__: List[str]
# TODO: Properly handle the `casting`-based combinatorics
# TODO: We need to evaluate the content `__subscripts` in order
# to identify whether or an array or scalar is returned. At a cursory
# glance this seems like something that can quite easilly be done with
# a mypy plugin.
# Something like `is_scalar = bool(__subscripts.partition("->")[-1])`
@overload
def einsum(
__subscripts: str,
*operands: _ArrayLikeBool_co,
out: None = ...,
dtype: Optional[_DTypeLikeBool] = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
__subscripts: str,
*operands: _ArrayLikeUInt_co,
out: None = ...,
dtype: Optional[_DTypeLikeUInt] = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
__subscripts: str,
*operands: _ArrayLikeInt_co,
out: None = ...,
dtype: Optional[_DTypeLikeInt] = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
__subscripts: str,
*operands: _ArrayLikeFloat_co,
out: None = ...,
dtype: Optional[_DTypeLikeFloat] = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
__subscripts: str,
*operands: _ArrayLikeComplex_co,
out: None = ...,
dtype: Optional[_DTypeLikeComplex] = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
__subscripts: str,
*operands: Any,
casting: _CastingUnsafe,
dtype: Optional[_DTypeLikeComplex_co] = ...,
out: None = ...,
order: _OrderKACF = ...,
optimize: _OptimizeKind = ...,
) -> Any: ...
@overload
def einsum(
__subscripts: str,
*operands: _ArrayLikeComplex_co,
out: _ArrayType,
dtype: Optional[_DTypeLikeComplex_co] = ...,
order: _OrderKACF = ...,
casting: _CastingSafe = ...,
optimize: _OptimizeKind = ...,
) -> _ArrayType: ...
@overload
def einsum(
__subscripts: str,
*operands: Any,
out: _ArrayType,
casting: _CastingUnsafe,
dtype: Optional[_DTypeLikeComplex_co] = ...,
order: _OrderKACF = ...,
optimize: _OptimizeKind = ...,
) -> _ArrayType: ...
# NOTE: `einsum_call` is a hidden kwarg unavailable for public use.
# It is therefore excluded from the signatures below.
# NOTE: In practice the list consists of a `str` (first element)
# and a variable number of integer tuples.
def einsum_path(
__subscripts: str,
*operands: _ArrayLikeComplex_co,
optimize: _OptimizeKind = ...,
) -> Tuple[List[Any], str]: ...

3789
.venv/lib/python3.9/site-packages/numpy/core/fromnumeric.py Normal file
View File

File diff suppressed because it is too large Load Diff

361
.venv/lib/python3.9/site-packages/numpy/core/fromnumeric.pyi Normal file
View File

@@ -0,0 +1,361 @@
import sys
import datetime as dt
from typing import Optional, Union, Sequence, Tuple, Any, overload, TypeVar
from numpy import (
ndarray,
number,
integer,
intp,
bool_,
generic,
_OrderKACF,
_OrderACF,
_ModeKind,
_PartitionKind,
_SortKind,
_SortSide,
)
from numpy.typing import (
DTypeLike,
ArrayLike,
_ShapeLike,
_Shape,
_ArrayLikeBool_co,
_ArrayLikeInt_co,
_NumberLike_co,
)
if sys.version_info >= (3, 8):
from typing import Literal
else:
from typing_extensions import Literal
# Various annotations for scalars
# While dt.datetime and dt.timedelta are not technically part of NumPy,
# they are one of the rare few builtin scalars which serve as valid return types.
# See https://github.com/numpy/numpy-stubs/pull/67#discussion_r412604113.
_ScalarNumpy = Union[generic, dt.datetime, dt.timedelta]
_ScalarBuiltin = Union[str, bytes, dt.date, dt.timedelta, bool, int, float, complex]
_Scalar = Union[_ScalarBuiltin, _ScalarNumpy]
# Integers and booleans can generally be used interchangeably
_ScalarGeneric = TypeVar("_ScalarGeneric", bound=generic)
_Number = TypeVar("_Number", bound=number)
# The signature of take() follows a common theme with its overloads:
# 1. A generic comes in; the same generic comes out
# 2. A scalar comes in; a generic comes out
# 3. An array-like object comes in; some keyword ensures that a generic comes out
# 4. An array-like object comes in; an ndarray or generic comes out
def take(
a: ArrayLike,
indices: _ArrayLikeInt_co,
axis: Optional[int] = ...,
out: Optional[ndarray] = ...,
mode: _ModeKind = ...,
) -> Any: ...
def reshape(
a: ArrayLike,
newshape: _ShapeLike,
order: _OrderACF = ...,
) -> ndarray: ...
def choose(
a: _ArrayLikeInt_co,
choices: ArrayLike,
out: Optional[ndarray] = ...,
mode: _ModeKind = ...,
) -> Any: ...
def repeat(
a: ArrayLike,
repeats: _ArrayLikeInt_co,
axis: Optional[int] = ...,
) -> ndarray: ...
def put(
a: ndarray,
ind: _ArrayLikeInt_co,
v: ArrayLike,
mode: _ModeKind = ...,
) -> None: ...
def swapaxes(
a: ArrayLike,
axis1: int,
axis2: int,
) -> ndarray: ...
def transpose(
a: ArrayLike,
axes: Union[None, Sequence[int], ndarray] = ...
) -> ndarray: ...
def partition(
a: ArrayLike,
kth: _ArrayLikeInt_co,
axis: Optional[int] = ...,
kind: _PartitionKind = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> ndarray: ...
def argpartition(
a: ArrayLike,
kth: _ArrayLikeInt_co,
axis: Optional[int] = ...,
kind: _PartitionKind = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> Any: ...
def sort(
a: ArrayLike,
axis: Optional[int] = ...,
kind: Optional[_SortKind] = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> ndarray: ...
def argsort(
a: ArrayLike,
axis: Optional[int] = ...,
kind: Optional[_SortKind] = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> ndarray: ...
@overload
def argmax(
a: ArrayLike,
axis: None = ...,
out: Optional[ndarray] = ...,
) -> intp: ...
@overload
def argmax(
a: ArrayLike,
axis: Optional[int] = ...,
out: Optional[ndarray] = ...,
) -> Any: ...
@overload
def argmin(
a: ArrayLike,
axis: None = ...,
out: Optional[ndarray] = ...,
) -> intp: ...
@overload
def argmin(
a: ArrayLike,
axis: Optional[int] = ...,
out: Optional[ndarray] = ...,
) -> Any: ...
@overload
def searchsorted(
a: ArrayLike,
v: _Scalar,
side: _SortSide = ...,
sorter: Optional[_ArrayLikeInt_co] = ..., # 1D int array
) -> intp: ...
@overload
def searchsorted(
a: ArrayLike,
v: ArrayLike,
side: _SortSide = ...,
sorter: Optional[_ArrayLikeInt_co] = ..., # 1D int array
) -> ndarray: ...
def resize(
a: ArrayLike,
new_shape: _ShapeLike,
) -> ndarray: ...
@overload
def squeeze(
a: _ScalarGeneric,
axis: Optional[_ShapeLike] = ...,
) -> _ScalarGeneric: ...
@overload
def squeeze(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
) -> ndarray: ...
def diagonal(
a: ArrayLike,
offset: int = ...,
axis1: int = ...,
axis2: int = ..., # >= 2D array
) -> ndarray: ...
def trace(
a: ArrayLike, # >= 2D array
offset: int = ...,
axis1: int = ...,
axis2: int = ...,
dtype: DTypeLike = ...,
out: Optional[ndarray] = ...,
) -> Any: ...
def ravel(a: ArrayLike, order: _OrderKACF = ...) -> ndarray: ...
def nonzero(a: ArrayLike) -> Tuple[ndarray, ...]: ...
def shape(a: ArrayLike) -> _Shape: ...
def compress(
condition: ArrayLike, # 1D bool array
a: ArrayLike,
axis: Optional[int] = ...,
out: Optional[ndarray] = ...,
) -> ndarray: ...
@overload
def clip(
a: ArrayLike,
a_min: ArrayLike,
a_max: Optional[ArrayLike],
out: Optional[ndarray] = ...,
**kwargs: Any,
) -> Any: ...
@overload
def clip(
a: ArrayLike,
a_min: None,
a_max: ArrayLike,
out: Optional[ndarray] = ...,
**kwargs: Any,
) -> Any: ...
def sum(
a: ArrayLike,
axis: _ShapeLike = ...,
dtype: DTypeLike = ...,
out: Optional[ndarray] = ...,
keepdims: bool = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
@overload
def all(
a: ArrayLike,
axis: None = ...,
out: None = ...,
keepdims: Literal[False] = ...,
) -> bool_: ...
@overload
def all(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
out: Optional[ndarray] = ...,
keepdims: bool = ...,
) -> Any: ...
@overload
def any(
a: ArrayLike,
axis: None = ...,
out: None = ...,
keepdims: Literal[False] = ...,
) -> bool_: ...
@overload
def any(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
out: Optional[ndarray] = ...,
keepdims: bool = ...,
) -> Any: ...
def cumsum(
a: ArrayLike,
axis: Optional[int] = ...,
dtype: DTypeLike = ...,
out: Optional[ndarray] = ...,
) -> ndarray: ...
def ptp(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
out: Optional[ndarray] = ...,
keepdims: bool = ...,
) -> Any: ...
def amax(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
out: Optional[ndarray] = ...,
keepdims: bool = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
def amin(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
out: Optional[ndarray] = ...,
keepdims: bool = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
# TODO: `np.prod()``: For object arrays `initial` does not necessarily
# have to be a numerical scalar.
# The only requirement is that it is compatible
# with the `.__mul__()` method(s) of the passed array's elements.
# Note that the same situation holds for all wrappers around
# `np.ufunc.reduce`, e.g. `np.sum()` (`.__add__()`).
def prod(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
dtype: DTypeLike = ...,
out: Optional[ndarray] = ...,
keepdims: bool = ...,
initial: _NumberLike_co = ...,
where: _ArrayLikeBool_co = ...,
) -> Any: ...
def cumprod(
a: ArrayLike,
axis: Optional[int] = ...,
dtype: DTypeLike = ...,
out: Optional[ndarray] = ...,
) -> ndarray: ...
def ndim(a: ArrayLike) -> int: ...
def size(a: ArrayLike, axis: Optional[int] = ...) -> int: ...
def around(
a: ArrayLike,
decimals: int = ...,
out: Optional[ndarray] = ...,
) -> Any: ...
def mean(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
dtype: DTypeLike = ...,
out: Optional[ndarray] = ...,
keepdims: bool = ...,
) -> Any: ...
def std(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
dtype: DTypeLike = ...,
out: Optional[ndarray] = ...,
ddof: int = ...,
keepdims: bool = ...,
) -> Any: ...
def var(
a: ArrayLike,
axis: Optional[_ShapeLike] = ...,
dtype: DTypeLike = ...,
out: Optional[ndarray] = ...,
ddof: int = ...,
keepdims: bool = ...,
) -> Any: ...

Some files were not shown because too many files have changed in this diff Show More